Hubbry Logo
Antivirus softwareAntivirus softwareMain
Open search
Antivirus software
Community hub
Antivirus software
logo
8 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Antivirus software
Antivirus software
from Wikipedia

ClamTk, an open-source antivirus based on the ClamAV antivirus engine, was originally developed by Tomasz Kojm in 2001.

Antivirus software (abbreviated to AV software), also known as anti-malware, is a computer program used to prevent, detect, and remove malware.

Antivirus software was originally developed to detect and remove computer viruses, hence the name. However, with the proliferation of other malware, antivirus software started to protect against other computer threats. Some products also include protection from malicious URLs, spam, and phishing.[1]

History

[edit]

1971–1980 period (pre-antivirus days)

[edit]

The first known computer virus appeared in 1971 and was dubbed the "Creeper virus".[2] This computer virus infected Digital Equipment Corporation's (DEC) PDP-10 mainframe computers running the TENEX operating system.[3][4]

The Creeper virus was eventually deleted by a program created by Ray Tomlinson and known as "The Reaper".[5] Some people consider "The Reaper" the first antivirus software ever written – it may be the case, but it is important to note that the Reaper was actually a virus itself specifically designed to remove the Creeper virus.[5][6]

The Creeper virus was followed by several other viruses. The first known that appeared "in the wild" was "Elk Cloner", in 1981, which infected Apple II computers.[7][8][9]

In 1983, the term "computer virus" was coined by Fred Cohen in one of the first ever published academic papers on computer viruses.[10] Cohen used the term "computer virus" to describe programs that: "affect other computer programs by modifying them in such a way as to include a (possibly evolved) copy of itself."[11] (note that a more recent definition of computer virus has been given by the Hungarian security researcher Péter Szőr: "a code that recursively replicates a possibly evolved copy of itself").[12][13]

The first IBM PC compatible "in the wild" computer virus, and one of the first real widespread infections, was "Brain" in 1986. From then, the number of viruses has grown exponentially.[14][15] Most of the computer viruses written in the early and mid-1980s were limited to self-reproduction and had no specific damage routine built into the code. That changed when more and more programmers became acquainted with computer virus programming and created viruses that manipulated or even destroyed data on infected computers.[16]

Before internet connectivity was widespread, computer viruses were typically spread by infected floppy disks. Antivirus software came into use, but was updated relatively infrequently. During this time, virus checkers essentially had to check executable files and the boot sectors of floppy disks and hard disks. However, as internet usage became common, viruses began to spread online.[17]

1980–1990 period (early days)

[edit]

There are competing claims for the innovator of the first antivirus product. Possibly, the first publicly documented removal of an "in the wild" computer virus (the "Vienna virus") was performed by Bernd Fix in 1987.[18][19]

In 1987, Andreas Lüning and Kai Figge, who founded G Data Software in 1985, released their first antivirus product for the Atari ST platform.[20] In 1987, the Ultimate Virus Killer (UVK) was also released.[21] This was the de facto industry standard virus killer for the Atari ST and Atari Falcon, the last version of which (version 9.0) was released in April 2004.[citation needed] In 1987, in the United States, John McAfee founded the McAfee company and, at the end of that year, he released the first version of VirusScan.[22] Also in 1987 (in Czechoslovakia), Peter Paško, Rudolf Hrubý, and Miroslav Trnka created the first version of NOD antivirus.[23][24]

In 1987, Fred Cohen wrote that there is no algorithm that can perfectly detect all possible computer viruses.[25]

Finally, at the end of 1987, the first two heuristic antivirus utilities were released: Flushot Plus by Ross Greenberg[26][27][28] and Anti4us by Erwin Lanting.[29] In his O'Reilly book, Malicious Mobile Code: Virus Protection for Windows, Roger Grimes described Flushot Plus as "the first holistic program to fight malicious mobile code (MMC)."[30]

However, the kind of heuristic used by early AV engines was totally different from those used today. The first product with a heuristic engine resembling modern ones was F-PROT in 1991.[31] Early heuristic engines were based on dividing the binary into different sections: data section, code section (in a legitimate binary, it usually starts always from the same location). Indeed, the initial viruses re-organized the layout of the sections, or overrode the initial portion of a section in order to jump to the very end of the file where malicious code was located—only going back to resume execution of the original code. This was a very specific pattern, not used at the time by any legitimate software, which represented an elegant heuristic to catch suspicious code. Other kinds of more advanced heuristics were later added, such as suspicious section names, incorrect header size, regular expressions, and partial pattern in-memory matching.

In 1988, the growth of antivirus companies continued. In Germany, Tjark Auerbach founded Avira (H+BEDV at the time) and released the first version of AntiVir (named "Luke Filewalker" at the time). In Bulgaria, Vesselin Bontchev released his first freeware antivirus program (he later joined FRISK Software). Also Frans Veldman released the first version of ThunderByte Antivirus, also known as TBAV (he sold his company to Norman Safeground in 1998). In Czechoslovakia, Pavel Baudiš and Eduard Kučera founded Avast Software (at the time ALWIL Software) and released their first version of avast! antivirus. In June 1988, in South Korea, Ahn Cheol-Soo released its first antivirus software, called V1 (he founded AhnLab later in 1995). Finally, in autumn 1988, in the United Kingdom, Alan Solomon founded S&S International and created his Dr. Solomon's Anti-Virus Toolkit (although he launched it commercially only in 1991 – in 1998 Solomon's company was acquired by McAfee, then known as Network Associates Inc.).

Also in 1988, a mailing list named VIRUS-L[32] was started on the BITNET/EARN network where new viruses and the possibilities of detecting and eliminating viruses were discussed. Some members of this mailing list were: Alan Solomon, Eugene Kaspersky (Kaspersky Lab), Friðrik Skúlason (FRISK Software), John McAfee (McAfee), Luis Corrons (Panda Security), Mikko Hyppönen (F-Secure), Péter Szőr, Tjark Auerbach (Avira) and Vesselin Bontchev (FRISK Software).[32]

In 1989, in Iceland, Friðrik Skúlason created the first version of F-PROT Anti-Virus (he founded FRISK Software only in 1993). Meanwhile, in the United States, Symantec (founded by Gary Hendrix in 1982) launched its first Symantec antivirus for Macintosh (SAM).[33][34] SAM 2.0, released March 1990, incorporated technology allowing users to easily update SAM to intercept and eliminate new viruses, including many that didn't exist at the time of the program's release.[35]

In the end of the 1980s, in United Kingdom, Jan Hruska and Peter Lammer founded the security firm Sophos and began producing their first antivirus and encryption products. In the same period, in Hungary, VirusBuster was founded (and subsequently incorporated by Sophos).[36]

1990–2000 period (emergence of the antivirus industry)

[edit]

In 1990, in Spain, Mikel Urizarbarrena founded Panda Security (Panda Software at the time).[37] In Hungary, the security researcher Péter Szőr released the first version of Pasteur antivirus.

In 1990, the Computer Antivirus Research Organization (CARO) was founded. In 1991, CARO released the "Virus Naming Scheme", originally written by Friðrik Skúlason and Vesselin Bontchev.[38] Although this naming scheme is now outdated, it remains the only existing standard that most computer security companies and researchers ever attempted to adopt. CARO members includes: Alan Solomon, Costin Raiu, Dmitry Gryaznov, Eugene Kaspersky, Friðrik Skúlason, Igor Muttik, Mikko Hyppönen, Morton Swimmer, Nick FitzGerald, Padgett Peterson, Peter Ferrie, Righard Zwienenberg and Vesselin Bontchev.[39][40]

In 1991, in the United States, Symantec released the first version of Norton AntiVirus. In the same year, in the Czech Republic, Jan Gritzbach and Tomáš Hofer founded AVG Technologies (Grisoft at the time), although they released the first version of their Anti-Virus Guard (AVG) only in 1992. On the other hand, in Finland, F-Secure (founded in 1988 by Petri Allas and Risto Siilasmaa – with the name of Data Fellows) released the first version of their antivirus product. F-Secure claims to be the first antivirus firm to establish a presence on the World Wide Web.[41]

In 1991, the European Institute for Computer Antivirus Research (EICAR) was founded to further antivirus research and improve development of antivirus software.[42][43]

In 1992, in Russia, Igor Danilov released the first version of SpiderWeb, which later became Dr.Web.[44]

In 1994, AV-TEST reported that there were 28,613 unique malware samples (based on MD5) in their database.[45]

Over time other companies were founded. In 1996, in Romania, Bitdefender was founded and released the first version of Anti-Virus eXpert (AVX).[46] In 1997, in Russia, Eugene Kaspersky and Natalya Kaspersky co-founded security firm Kaspersky Lab.[47]

In 1996, there was also the first "in the wild" Linux virus, known as "Staog".[48]

In 1999, AV-TEST reported that there were 98,428 unique malware samples (based on MD5) in their database.[45]

2000–2005 period

[edit]

In 2000, Rainer Link and Howard Fuhs started the first open source antivirus engine, called OpenAntivirus Project.[49]

In 2001, Tomasz Kojm released the first version of ClamAV, the first ever open source antivirus engine to be commercialised. In 2007, ClamAV was bought by Sourcefire,[50] which in turn was acquired by Cisco Systems in 2013.[51]

In 2002, in United Kingdom, Morten Lund and Theis Søndergaard co-founded the antivirus firm BullGuard.[52]

In 2005, AV-TEST reported that there were 333,425 unique malware samples (based on MD5) in their database.[45]

2005–2014 period

[edit]

In 2007, AV-TEST reported a number of 5,490,960 new unique malware samples (based on MD5) only for that year.[45] In 2012 and 2013, antivirus firms reported a new malware samples range from 300,000 to over 500,000 per day.[53][54]

Over the years it has become necessary for antivirus software to use several different strategies (e.g. specific email and network protection or low level modules) and detection algorithms, as well as to check an increasing variety of files, rather than just executables, for several reasons:

  • Powerful macros used in word processor applications, such as Microsoft Word, presented a risk. Virus writers could use the macros to write viruses embedded within documents. This meant that computers could now also be at risk from infection by opening documents with hidden attached macros.[55]
  • The possibility of embedding executable objects inside otherwise non-executable file formats can make opening those files a risk.[56]
  • Later email programs, in particular Microsoft's Outlook Express and Outlook, were vulnerable to viruses embedded in the email body itself. A user's computer could be infected by just opening or previewing a message.[57]

In 2005, F-Secure was the first security firm that developed an Anti-Rootkit technology, called BlackLight.

Because most users are usually connected to the Internet on a continual basis, Jon Oberheide first proposed a Cloud-based antivirus design in 2008.[58]

In February 2008 McAfee Labs added the industry-first cloud-based anti-malware functionality to VirusScan under the name Artemis. It was tested by AV-Comparatives in February 2008[59] and officially unveiled in August 2008 in McAfee VirusScan.[60]

Cloud AV created problems for comparative testing of security software – part of the AV definitions was out of testers control (on constantly updated AV company servers) thus making results non-repeatable. As a result, Anti-Malware Testing Standards Organisation (AMTSO) started working on method of testing cloud products which was adopted on May 7, 2009.[61]

In 2011, AVG introduced a similar cloud service, called Protective Cloud Technology.[62]

2014–present: rise of next-gen, market consolidation

[edit]

Following the 2013 release of the APT 1 report from Mandiant, the industry has seen a shift towards signature-less approaches to the problem capable of detecting and mitigating zero-day attacks.[63] Numerous approaches to address these new forms of threats have appeared, including behavioral detection, artificial intelligence, machine learning, and cloud-based file detection. According to Gartner, it is expected the rise of new entrants, such Carbon Black, Cylance and Crowdstrike will force end point protection incumbents into a new phase of innovation and acquisition.[64]

One method from Bromium involves micro-virtualization to protect desktops from malicious code execution initiated by the end user. Another approach from SentinelOne and Carbon Black focuses on behavioral detection by building a full context around every process execution path in real time,[65][66] while Cylance leverages an artificial intelligence model based on machine learning.[67]

Increasingly, these signature-less approaches have been defined by the media and analyst firms as "next-generation" antivirus[68] and are seeing rapid market adoption as certified antivirus replacement technologies by firms such as Coalfire and DirectDefense.[69] In response, traditional antivirus vendors such as Trend Micro,[70] Symantec and Sophos[71] have responded by incorporating "next-gen" offerings into their portfolios as analyst firms such as Forrester and Gartner have called traditional signature-based antivirus "ineffective" and "outdated".[72]

As of Windows 8, Windows includes its own free antivirus protection under the Windows Defender brand. Despite bad detection scores in its early days, AV-Test now certifies Defender as one of its top products.[73][74] While it isn't publicly known how the inclusion of antivirus software in Windows affected antivirus sales, Google search traffic for antivirus has declined significantly since 2010.[75] In 2014, Intel bought McAfee.[76]

Since 2016, there has been a notable amount of consolidation in the industry. Avast purchased AVG in 2016 for $1.3 billion.[77] Avira was acquired by Norton owner Gen Digital (then NortonLifeLock) in 2020 for $360 million.[78] In 2021, the Avira division of Gen Digital acquired BullGuard.[79] The BullGuard brand was discontinued in 2022 and its customers were migrated to Norton. In 2022, Gen Digital acquired Avast, effectively consolidating four major antivirus brands under one owner.[80]

In September 2024, following the US Commerce Department's ban on Kaspersky, Pango Group acquired its customers (about 1 million).[81] The customers received continued services with no action required on their part. Then, in December 2024, Pango Group merged with Total Security, the provider of Total AV antivirus. The combined entity, now called Point Wild, has an enterprise value of $1.7 billion.[82]

As of 2024, more than half of Americans use built-in antivirus protection for their devices like Microsoft Defender or XProtect from Apple. However, about 121 million adults still use third-party antivirus software. Half of these adults use paid products, and about 50% of third-party software users - the owners of personal computers and Windows operating systems.[83] Antivirus programs on mobile devices are used by 17% of adults.[84]

The 2025 antivirus market report confirms that most third-party antivirus users are on desktop devices, primarily aged between 35 and 45. In contrast, younger users (18–25) tend to rely on ad blockers instead. In the U.S., on average, 75–85% of people use antivirus software or some other form of protection on at least one device. Antivirus software for computers and mobile devices is predominantly used by residents of large cities. Mobile device users more often rely on password managers rather than antivirus software for digital security. Moreover, the majority of password‑manager users live in medium‑sized and small towns.[85]

Identification methods

[edit]

In 1987, Frederick B. Cohen demonstrated that the algorithm which would be able to detect all possible viruses can't possibly exist (like the algorithm which determines whether or not the given program halts).[25] However, using different layers of defense, a good detection rate may be achieved.

There are several methods which antivirus engines can use to identify malware:

  • Sandbox detection: a particular behavioural-based detection technique that, instead of detecting the behavioural fingerprint at run time, it executes the programs in a virtual environment, logging what actions the program performs. Depending on the actions logged which can include memory usage and network accesses,[86] the antivirus engine can determine if the program is malicious or not.[87] If not, then, the program is executed in the real environment. Although this technique has shown to be quite effective, given its heaviness and slowness, it is rarely used in end-user antivirus solutions.[88]
  • Data mining techniques: one of the latest approaches applied in malware detection. Data mining and machine learning algorithms are used to try to classify the behaviour of a file (as either malicious or benign) given a series of file features, that are extracted from the file itself.[89][90][91]

Signature-based detection

[edit]

Traditional antivirus software relies heavily upon signatures to identify malware.[92]

Substantially, when a malware sample arrives in the hands of an antivirus firm, it is analysed by malware researchers or by dynamic analysis systems. Then, once it is determined to be a malware, a proper signature of the file is extracted and added to the signatures database of the antivirus software.[93]

Although the signature-based approach can effectively contain malware outbreaks, malware authors have tried to stay a step ahead of such software by writing "oligomorphic", "polymorphic" and, more recently, "metamorphic" viruses, which encrypt parts of themselves or otherwise modify themselves as a method of disguise, so as to not match virus signatures in the dictionary.[94]

Heuristics

[edit]

Many viruses start as a single infection and through either mutation or refinements by other attackers, can grow into dozens of slightly different strains, called variants. Generic detection refers to the detection and removal of multiple threats using a single virus definition.[95]

For example, the Vundo trojan has several family members, depending on the antivirus vendor's classification. Symantec classifies members of the Vundo family into two distinct categories, Trojan.Vundo and Trojan.Vundo.B.[96][97]

While it may be advantageous to identify a specific virus, it can be quicker to detect a virus family through a generic signature or through an inexact match to an existing signature. Virus researchers find common areas that all viruses in a family share uniquely and can thus create a single generic signature. These signatures often contain non-contiguous code, using wildcard characters where differences lie. These wildcards allow the scanner to detect viruses even if they are padded with extra, meaningless code.[98] A detection that uses this method is said to be "heuristic detection".

Rootkit detection

[edit]

Anti-virus software can attempt to scan for rootkits. A rootkit is a type of malware designed to gain administrative-level control over a computer system without being detected. Rootkits can change how the operating system functions and in some cases can tamper with the anti-virus program and render it ineffective. Rootkits are also difficult to remove, in some cases requiring a complete re-installation of the operating system.[99]

Real-time protection

[edit]

Real-time protection, on-access scanning, background guard, resident shield, autoprotect, and other synonyms refer to the automatic protection provided by most antivirus, anti-spyware, and other anti-malware programs. This monitors computer systems for suspicious activity such as computer viruses, spyware, adware, and other malicious objects. Real-time protection detects threats in opened files and scans apps in real-time as they are installed on the device.[100] When inserting a CD, opening an email, or browsing the web, or when a file already on the computer is opened or executed.[101]

Machine learning detection

[edit]

Machine learning has emerged as a core detection method in modern antivirus software, using algorithms trained on large datasets to classify software as malicious or benign. ML-based approaches are diverse, but detectors typically extract features from files, such as API call sequences, byte n-grams, opcode distributions, behavioral characteristics, or even raw bytes, and train classifiers to identify malware based on learned patterns from this data.​[102]

ML-based detection can be highly effective, but still faces significant challenges. Concept drift occurs as malware continuously evolves, causing trained models to degrade in accuracy over time without regular retraining on fresh samples.[103] Research has demonstrated that even simple obfuscation techniques can create adversarial variants that bypass ML-based detectors while preserving malicious functionality.[104] Additionally, the highly imbalanced nature of real-world data, where benign files vastly outnumber malicious ones, makes acquiring training data difficult and requires careful tuning to avoid unacceptable false positive rates.[105]

Issues of concern

[edit]

Unexpected renewal costs

[edit]

Some commercial antivirus software end-user license agreements include a clause that the subscription will be automatically renewed, and the purchaser's credit card automatically billed, at the renewal time without explicit approval. For example, McAfee requires users to unsubscribe at least 60 days before the expiration of the present subscription,[106] while Bitdefender sends notifications to unsubscribe 30 days before the renewal.[107] Norton AntiVirus also renews subscriptions automatically by default.[108]

Rogue security applications

[edit]

Some apparent antivirus programs are actually malware masquerading as legitimate software, such as WinFixer, MS Antivirus, and Mac Defender.[109]

Problems caused by false positives

[edit]

A "false positive" or "false alarm" is when antivirus software identifies a non-malicious file as malware. When this happens, it can cause serious problems. For example, if an antivirus program is configured to immediately delete or quarantine infected files, as is common on Microsoft Windows antivirus applications, a false positive in an essential file can render the Windows operating system or some applications unusable.[110] Recovering from such damage to critical software infrastructure incurs technical support costs and businesses can be forced to close whilst remedial action is undertaken.[111][112]

Examples of serious false-positives:

  • May 2007: a faulty virus signature issued by Symantec mistakenly removed essential operating system files, leaving thousands of PCs unable to boot.[113]
  • May 2007: the executable file required by Pegasus Mail on Windows was falsely detected by Norton AntiVirus as being a Trojan and it was automatically removed, preventing Pegasus Mail from running. Norton AntiVirus had falsely identified three releases of Pegasus Mail as malware, and would delete the Pegasus Mail installer file when that happened.[114] In response to this Pegasus Mail stated:

On the basis that Norton/Symantec has done this for every one of the last three releases of Pegasus Mail, we can only condemn this product as too flawed to use, and recommend in the strongest terms that our users cease using it in favour of alternative, less buggy anti-virus packages.[114]

  • April 2010: McAfee VirusScan detected svchost.exe, a normal Windows binary, as a virus on machines running Windows XP with Service Pack 3, causing a reboot loop and loss of all network access.[115][116]
  • December 2010: a faulty update on the AVG anti-virus suite damaged 64-bit versions of Windows 7, rendering it unable to boot, due to an endless boot loop created.[117]
  • October 2011: Microsoft Security Essentials (MSE) removed the Google Chrome web browser, rival to Microsoft's own Internet Explorer. MSE flagged Chrome as a Zbot banking trojan.[118]
  • September 2012: Sophos' anti-virus suite identified various update-mechanisms, including its own, as malware. If it was configured to automatically delete detected files, Sophos Antivirus could render itself unable to update, required manual intervention to fix the problem.[119][120]
  • September 2017: the Google Play Protect anti-virus started identifying Motorola's Moto G4 Bluetooth application as malware, causing Bluetooth functionality to become disabled.[121]
  • September 2022: Microsoft Defender flagged all Chromium based web browsers and Electron based apps like WhatsApp, Discord, Spotify as a severe threat.[122]
[edit]

Running (the real-time protection of) multiple antivirus programs concurrently can degrade performance and create conflicts.[123] However, using a concept called multiscanning, several companies (including G Data Software[124] and Microsoft[125]) have created applications which can run multiple engines concurrently.

It is sometimes necessary to temporarily disable virus protection when installing major updates such as Windows Service Packs or updating graphics card drivers.[126] Active antivirus protection may partially or completely prevent the installation of a major update. Anti-virus software can cause problems during the installation of an operating system upgrade, e.g. when upgrading to a newer version of Windows "in place"—without erasing the previous version of Windows. Microsoft recommends that anti-virus software be disabled to avoid conflicts with the upgrade installation process.[127][128][129] Active anti-virus software can also interfere with a firmware update process.[130]

The functionality of a few computer programs can be hampered by active anti-virus software. For example, TrueCrypt, a disk encryption program, states on its troubleshooting page that anti-virus programs can conflict with TrueCrypt and cause it to malfunction or operate very slowly.[131] Anti-virus software can impair the performance and stability of games running in the Steam platform.[132]

Support issues also exist around antivirus application interoperability with common solutions like SSL VPN remote access and network access control products.[133] These technology solutions often have policy assessment applications that require an up-to-date antivirus to be installed and running. If the antivirus application is not recognized by the policy assessment, whether because the antivirus application has been updated or because it is not part of the policy assessment library, the user will be unable to connect.

Effectiveness

[edit]

Studies in December 2007 showed that the effectiveness of antivirus software had decreased in the previous year, particularly against unknown or zero day attacks. The computer magazine c't found that detection rates for these threats had dropped from 40 to 50% in 2006 to 20–30% in 2007. At that time, the only exception was the NOD32 antivirus, which managed a detection rate of 68%.[134] According to the ZeuS tracker website the average detection rate for all variants of the ZeuS trojan is as low as 40%.[135][independent source needed]

The problem is magnified by the changing intent of virus authors. Some years ago it was obvious when a virus infection was present. At the time, viruses were written by amateurs and exhibited destructive behavior or pop-ups. Modern viruses are often written by professionals, financed by criminal organizations.[136]

In 2008, Eva Chen, CEO of Trend Micro, stated that the anti-virus industry has over-hyped how effective its products are—and so has been misleading customers—for years.[137]

Independent testing on all the major virus scanners consistently shows that none provides 100% virus detection. The best ones provided as high as 99.9% detection for simulated real-world situations, while the lowest provided 91.1% in tests conducted in August 2013. Many virus scanners produce false positive results as well, identifying benign files as malware.[138]

Although methods may differ, some notable independent quality testing agencies include AV-Comparatives, ICSA Labs, SE Labs, West Coast Labs, Virus Bulletin, AV-TEST and other members of the Anti-Malware Testing Standards Organization.[139][140]

New viruses

[edit]

Anti-virus programs are not always effective against new viruses, even those that use non-signature-based methods that should detect new viruses. The reason for this is that the virus designers test their new viruses on the major anti-virus applications to make sure that they are not detected before releasing them into the wild.[141]

Some new viruses, particularly ransomware, use polymorphic code to avoid detection by virus scanners. Jerome Segura, a security analyst with ParetoLogic, explained:[142]

It's something that they miss a lot of the time because this type of [ransomware virus] comes from sites that use a polymorphism, which means they basically randomize the file they send you and it gets by well-known antivirus products very easily. I've seen people firsthand getting infected, having all the pop-ups and yet they have antivirus software running and it's not detecting anything. It actually can be pretty hard to get rid of, as well, and you're never really sure if it's really gone. When we see something like that usually we advise to reinstall the operating system or reinstall backups.[142]

A proof of concept virus has used the Graphics Processing Unit (GPU) to avoid detection from anti-virus software. The potential success of this involves bypassing the CPU in order to make it much harder for security researchers to analyse the inner workings of such malware.[143]

Rootkits

[edit]

Detecting rootkits is a major challenge for anti-virus programs. Rootkits have full administrative access to the computer and are invisible to users and hidden from the list of running processes in the task manager. Rootkits can modify the inner workings of the operating system and tamper with antivirus programs.[144]

Damaged files

[edit]

If a file has been infected by a computer virus, anti-virus software will attempt to remove the virus code from the file during disinfection, but it is not always able to restore the file to its undamaged state.[145][146] In such circumstances, damaged files can only be restored from existing backups or shadow copies (this is also true for ransomware[147]); installed software that is damaged requires re-installation[148] (however, see System File Checker).

Firmware infections

[edit]

Any writeable firmware in the computer can be infected by malicious code.[149] This is a major concern, as an infected BIOS could require the actual BIOS chip to be replaced to ensure the malicious code is completely removed.[150] Anti-virus software is not effective at protecting firmware and the motherboard BIOS from infection.[151] In 2014, security researchers discovered that USB devices contain writeable firmware which can be modified with malicious code (dubbed "BadUSB"), which anti-virus software cannot detect or prevent. The malicious code can run undetected on the computer and could even infect the operating system prior to it booting up.[152][153]

Performance and other drawbacks

[edit]

Antivirus software has some drawbacks, first of which that it can impact a computer's performance.[154]

Furthermore, inexperienced users can be lulled into a false sense of security when using the computer, considering their computers to be invulnerable, and may have problems understanding the prompts and decisions that antivirus software presents them with. An incorrect decision may lead to a security breach. If the antivirus software employs heuristic detection, it must be fine-tuned to minimize misidentifying harmless software as malicious (false positive).[155]

Antivirus software itself usually runs at the highly trusted kernel level of the operating system to allow it access to all the potential malicious process and files, creating a potential avenue of attack.[156] The US National Security Agency (NSA) and the UK Government Communications Headquarters (GCHQ) intelligence agencies, respectively, have been exploiting anti-virus software to spy on users.[157] Anti-virus software has highly privileged and trusted access to the underlying operating system, which makes it a much more appealing target for remote attacks.[158] Additionally anti-virus software is "years behind security-conscious client-side applications like browsers or document readers. It means that Acrobat Reader, Microsoft Word or Google Chrome are harder to exploit than 90 percent of the anti-virus products out there", according to Joxean Koret, a researcher with Coseinc, a Singapore-based information security consultancy.[158]

Alternative solutions

[edit]
The command-line virus scanner of ClamAV running a virus signature definition update, scanning a file, and identifying an EICAR test file

Antivirus software running on individual computers is the most common method employed of guarding against malware, but it is not the only solution. Other solutions can also be employed by users, including Unified Threat Management (UTM), hardware and network firewalls, Cloud-based antivirus, online scanners, and Content Disarm & Reconstruction (CDR).

Hardware and network firewall

[edit]

Network firewalls prevent unknown programs and processes from accessing the system. However, they are not antivirus systems and make no attempt to identify or remove anything. They may protect against infection from outside the protected computer or network, and limit the activity of any malicious software which is present by blocking incoming or outgoing requests on certain TCP/IP ports. A firewall is designed to deal with broader system threats that come from network connections into the system and is not an alternative to a virus protection system.

Cloud antivirus

[edit]

Cloud antivirus is a technology that uses lightweight agent software on the protected computer, while offloading the majority of data analysis to the provider's infrastructure.[159]

One approach to implementing cloud antivirus involves scanning suspicious files using multiple antivirus engines. This approach was proposed by an early implementation of the cloud antivirus concept called CloudAV. CloudAV was designed to send programs or documents to a network cloud where multiple antivirus and behavioral detection programs are used simultaneously in order to improve detection rates. Parallel scanning of files using potentially incompatible antivirus scanners is achieved by spawning a virtual machine per detection engine and therefore eliminating any possible issues. CloudAV can also perform "retrospective detection", whereby the cloud detection engine rescans all files in its file access history when a new threat is identified thus improving new threat detection speed. Finally, CloudAV is a solution for effective virus scanning on devices that lack the computing power to perform the scans themselves.[160]

Some examples of cloud anti-virus products are Panda Cloud Antivirus and Immunet. Comodo Group has also produced cloud-based anti-virus.[161][162]

Online scanning

[edit]

Some antivirus vendors maintain websites with free online scanning capability of the entire computer, critical areas only, local disks, folders or files. Periodic online scanning is a good idea for those that run antivirus applications on their computers because those applications are frequently slow to catch threats. One of the first things that malicious software does in an attack is disable any existing antivirus software and sometimes the only way to know of an attack is by turning to an online resource that is not installed on the infected computer.[163]

Content disarm & reconstruction

[edit]

Content Disarm & Reconstruction (CDR) technology protects a network from malware by removing components from inbound files which do not rigorously conform with the standards of that file type. It does so by rebuilding the original files without any illegitimate components present. Part of the CDR process may also involve flattening and converting the reconstructed files to Portable Document Format (PDF) for maximum safety.

CDR malware removal does not attempt to identify malware behavior before taking action; rather, it employs a zero-trust approach against files entering a network perimeter. This can make it an effective solution for protecting networks against zero-day vulnerabilities.[164]

Specialized tools

[edit]
The command-line rkhunter scanner is an engine to scan for Linux rootkits running on openSUSE.

Virus removal tools are available to help remove stubborn infections or a certain type of infection. Examples include Windows Malicious Software Removal Tool,[165] Kaspersky Virus Removal Tool,[166] and Sophos Scan & Clean.[167] It is also worth noting that sometimes antivirus software can produce a false-positive result, indicating an infection where there is none.[168]

A rescue disk that is bootable, such as a CD or USB storage device, can be used to run antivirus software outside of the installed operating system in order to remove infections while they are dormant. A bootable rescue disk can be useful when, for example, the installed operating system is no longer bootable or has malware that is resisting all attempts to be removed by the installed antivirus software. Examples of software that can be used on a bootable rescue disk include the Kaspersky Rescue Disk,[169] Trend Micro Rescue Disk,[170] and Comodo Rescue Disk.[171]

Usage and risks

[edit]

According to an FBI survey, major businesses lose $12 million annually dealing with virus incidents.[172] A survey by Symantec in 2009 found that a third of small to medium-sized business did not use antivirus protection at that time, whereas more than 80% of home users had some kind of antivirus installed.[173] According to a sociological survey conducted by G Data Software in 2010 49% of women did not use any antivirus program at all.[174]

See also

[edit]

Citations

[edit]
  1. ^ "What is antivirus software?". Microsoft. Archived from the original on April 11, 2011.
  2. ^ Thomas Chen, Jean-Marc Robert (2004). "The Evolution of Viruses and Worms". Archived from the original on May 17, 2009. Retrieved February 16, 2009.
  3. ^ From the first email to the first YouTube video: a definitive internet history Archived December 31, 2016, at the Wayback Machine. Tom Meltzer and Sarah Phillips. The Guardian. October 23, 2009
  4. ^ IEEE Annals of the History of Computing, Volumes 27–28. IEEE Computer Society, 2005. 74 Archived May 13, 2016, at the Wayback Machine: "[...]from one machine to another led to experimentation with the Creeper program, which became the world's first computer worm: a computation that used the network to recreate itself on another node, and spread from node to node."
  5. ^ a b Metcalf, John (2014). "Core War: Creeper & Reaper". Archived from the original on May 2, 2014. Retrieved May 1, 2014.
  6. ^ "Creeper – The Virus Encyclopedia". Archived from the original on September 20, 2015.
  7. ^ "Elk Cloner". Archived from the original on January 7, 2011. Retrieved December 10, 2010.
  8. ^ "Top 10 Computer Viruses: No. 10 – Elk Cloner". The Science Channel. Archived from the original on February 7, 2011. Retrieved December 10, 2010.
  9. ^ "List of Computer Viruses Developed in 1980s". Archived from the original on July 24, 2011. Retrieved December 10, 2010.
  10. ^ Fred Cohen: "Computer Viruses – Theory and Experiments" (1983) Archived June 8, 2011, at the Wayback Machine. Eecs.umich.edu (November 3, 1983). Retrieved on 2017-01-03.
  11. ^ Cohen, Fred (April 1, 1988). "Invited Paper: On the Implications of Computer Viruses and Methods of Defense". Computers & Security. 7 (2): 167–184. doi:10.1016/0167-4048(88)90334-3.
  12. ^ Szor 2005, p. [page needed].
  13. ^ "Virus Bulletin :: In memoriam: Péter Ször 1970–2013". Archived from the original on August 26, 2014.
  14. ^ Bassham, Lawrence; Polk, W. (October 1992). "History of Viruses". Nistir 4939. doi:10.6028/NIST.IR.4939. Archived from the original on April 23, 2011.
  15. ^ Leyden, John (January 19, 2006). "PC virus celebrates 20th birthday". The Register. Archived from the original on September 6, 2010. Retrieved March 21, 2011.
  16. ^ "The History of Computer Viruses". November 10, 2017.
  17. ^ Panda Security (April 2004). "(II) Evolution of computer viruses". Archived from the original on August 2, 2009. Retrieved June 20, 2009.
  18. ^ Kaspersky Lab Virus list. viruslist.com
  19. ^ Wells, Joe (August 30, 1996). "Virus timeline". IBM. Archived from the original on June 4, 2008. Retrieved June 6, 2008.
  20. ^ G Data Software AG (2017). "G Data presents first Antivirus solution in 1987". Archived from the original on March 15, 2017. Retrieved December 13, 2017.
  21. ^ Karsmakers, Richard (January 2010). "The ultimate Virus Killer Book and Software". Archived from the original on July 29, 2016. Retrieved July 6, 2016.
  22. ^ Cavendish, Marshall (2007). Inventors and Inventions, Volume 4. Paul Bernabeo. p. 1033. ISBN 978-0-7614-7767-9.
  23. ^ "About ESET Company". Archived from the original on October 28, 2016.
  24. ^ "ESET NOD32 Antivirus". Vision Square. February 16, 2016. Archived from the original on February 24, 2016.
  25. ^ a b Cohen, Fred, An Undetectable Computer Virus (Archived), 1987, IBM
  26. ^ Yevics, Patricia A. "Flu Shot for Computer Viruses". americanbar.org. Archived from the original on August 26, 2014.
  27. ^ Strom, David (April 1, 2010). "How friends help friends on the Internet: The Ross Greenberg Story". wordpress.com. Archived from the original on August 26, 2014.
  28. ^ "Anti-virus is 30 years old". spgedwards.com. April 2012. Archived from the original on April 27, 2015.
  29. ^ "A Brief History of Antivirus Software". techlineinfo.com. Archived from the original on August 26, 2014.
  30. ^ Grimes, Roger A. (June 1, 2001). Malicious Mobile Code: Virus Protection for Windows. O'Reilly Media, Inc. p. 522. ISBN 978-1-56592-682-0. Archived from the original on March 21, 2017.
  31. ^ "Friðrik Skúlason ehf" (in Icelandic). Archived from the original on June 17, 2006.
  32. ^ a b "The 'Security Digest' Archives (TM): www.phreak.org-virus_l". Archived from the original on January 5, 2010.
  33. ^ "Symantec Softwares and Internet Security at PCM". Archived from the original on July 1, 2014.
  34. ^ SAM Identifies Virus-Infected Files, Repairs Applications, InfoWorld, May 22, 1989
  35. ^ SAM Update Lets Users Program for New Viruses, InfoWorld, February 19, 1990
  36. ^ "VirusBuster Company Profile 2024: Valuation, Investors, Acquisition".
  37. ^ Naveen, Sharanya. "Panda Security". Archived from the original on June 30, 2016. Retrieved May 31, 2016.
  38. ^ "A New Virus Naming Convention (1991) – CARO – Computer Antivirus Research Organization". Archived from the original on August 13, 2011.
  39. ^ "CARO Members". CARO. Archived from the original on July 18, 2011. Retrieved June 6, 2011.
  40. ^ CAROids, Hamburg 2003 Archived November 7, 2014, at the Wayback Machine
  41. ^ "F-Secure Weblog: News from the Lab". F-secure.com. Archived from the original on September 23, 2012. Retrieved September 23, 2012.
  42. ^ "About EICAR". EICAR official website. Archived from the original on June 14, 2018. Retrieved October 28, 2013.
  43. ^ Harley, David; Myers, Lysa; Willems, Eddy. "Test Files and Product Evaluation: the Case for and against Malware Simulation" (PDF). AVAR2010 13th Association of anti Virus Asia Researchers International Conference. Archived from the original (PDF) on September 29, 2011. Retrieved June 30, 2011.
  44. ^ "Dr. Web LTD Doctor Web / Dr. Web Reviews, Best AntiVirus Software Reviews, Review Centre". Reviewcentre.com. Archived from the original on February 23, 2014. Retrieved February 17, 2014.
  45. ^ a b c d [In 1994, AV-Test.org reported 28,613 unique malware samples (based on MD5). "A Brief History of Malware; The First 25 Years"]
  46. ^ "BitDefender Product History". Archived from the original on March 17, 2012.
  47. ^ "InfoWatch Management". InfoWatch. Archived from the original on August 21, 2013. Retrieved August 12, 2013.
  48. ^ "Linuxvirus – Community Help Wiki". Archived from the original on March 24, 2017.
  49. ^ "Sorry – recovering..." Archived from the original on August 26, 2014.
  50. ^ "Sourcefire acquires ClamAV". ClamAV. August 17, 2007. Archived from the original on December 15, 2007. Retrieved February 12, 2008.
  51. ^ "Cisco Completes Acquisition of Sourcefire". cisco.com. October 7, 2013. Archived from the original on January 13, 2015. Retrieved June 18, 2014.
  52. ^ Der Unternehmer – brand eins online Archived November 22, 2012, at the Wayback Machine. Brandeins.de (July 2009). Retrieved on January 3, 2017.
  53. ^ Williams, Greg (April 2012). "The digital detective: Mikko Hypponen's war on malware is escalating". Wired. Archived from the original on March 15, 2016.
  54. ^ "Everyday cybercrime – and what you can do about it". September 16, 2013. Archived from the original on February 20, 2014.
  55. ^ Szor 2005, pp. 66–67.
  56. ^ "New virus travels in PDF files". August 7, 2001. Archived from the original on June 16, 2011. Retrieved October 29, 2011.
  57. ^ Slipstick Systems (February 2009). "Protecting Microsoft Outlook against Viruses". Archived from the original on June 2, 2009. Retrieved June 18, 2009.
  58. ^ "CloudAV: N-Version Antivirus in the Network Cloud". usenix.org. Archived from the original on August 26, 2014.
  59. ^ McAfee Artemis Preview Report Archived April 3, 2016, at the Wayback Machine. av-comparatives.org
  60. ^ McAfee Third Quarter 2008 Archived April 3, 2016, at the Wayback Machine. corporate-ir.net
  61. ^ "AMTSO Best Practices for Testing In-the-Cloud Security Products". AMTSO. Archived from the original on April 14, 2016. Retrieved March 21, 2016.
  62. ^ "TECHNOLOGY OVERVIEW". AVG Security. Archived from the original on June 2, 2015. Retrieved February 16, 2015.
  63. ^ Barrett, Brian (October 18, 2018). "The Mysterious Return of Years-Old Chinese Malware". Wired. Retrieved June 16, 2019 – via www.wired.com.
  64. ^ "Magic Quadrant Endpoint Protection Platforms 2016". Gartner Research.
  65. ^ Messmer, Ellen (August 20, 2014). "Start-up offers up endpoint detection and response for behavior-based malware detection". networkworld.com. Archived from the original on February 5, 2015.
  66. ^ "Homeland Security Today: Bromium Research Reveals Insecurity in Existing Endpoint Malware Protection Deployments". Archived from the original on September 24, 2015.
  67. ^ "Duelling Unicorns: CrowdStrike Vs. Cylance In Brutal Battle To Knock Hackers Out". Forbes. July 6, 2016. Archived from the original on September 11, 2016.
  68. ^ Potter, Davitt (June 9, 2016). "Is Anti-virus Dead? The Shift Toward Next-Gen Endpoints". Archived from the original on December 20, 2016.
  69. ^ "CylancePROTECT® Achieves HIPAA Security Rule Compliance Certification". Cylance. Archived from the original on October 22, 2016. Retrieved October 21, 2016.
  70. ^ "Trend Micro-XGen". Trend Micro. October 18, 2016. Archived from the original on December 21, 2016.
  71. ^ "Next-Gen Endpoint". Sophos. Archived from the original on November 6, 2016.
  72. ^ The Forrester Wave™: Endpoint Security Suites, Q4 2016 Archived October 22, 2016, at the Wayback Machine. Forrester.com (October 19, 2016). Retrieved on 2017-01-03.
  73. ^ Paul Wagenseil (May 25, 2016). "Is Windows Defender Good Enough? Not Yet". Tom's Guide. Retrieved December 18, 2023.
  74. ^ "Test antivirus software for Windows 11 - October 2023". www.av-test.org. Retrieved December 18, 2023.
  75. ^ "Google Trends". Google Trends. Archived from the original on December 18, 2023. Retrieved December 18, 2023.
  76. ^ "McAfee Becomes Intel Security". McAfee Inc. Archived from the original on January 15, 2014. Retrieved January 15, 2014.
  77. ^ "Avast Announces Agreement to Acquire AVG for $1.3B". Avast Announces Agreement to Acquire AVG for $1.3B. Retrieved December 18, 2023.
  78. ^ Lunden, Ingrid (December 7, 2020). "NortonLifeLock acquires Avira in $360M all-cash deal, 8 months after Avira was acquired for $180M". TechCrunch. Retrieved December 18, 2023.
  79. ^ Daniel Todd (February 7, 2022). "BullGuard to drop name in favour of Norton branding". channelpro. Retrieved December 18, 2023.
  80. ^ "NortonLifeLock Completes Merger with Avast". NortonLifeLock Completes Merger with Avast. Retrieved December 18, 2023.
  81. ^ "Kaspersky antivirus gets sold to U.S. firm Pango Group". Axios. September 5, 2024. Retrieved May 14, 2025.
  82. ^ "Pango Group Merges with Total Security; Combined Company Rebranded Point Wild". PR Newswire. June 26, 2024. Retrieved May 14, 2025.
  83. ^ "2024 Antivirus Trends, Statistics, and Merket Report". Security.org. Archived from the original on July 17, 2024. Retrieved November 24, 2024.
  84. ^ "2025 Antivirus Trends, Statistics, and Market Report". Security.org. Retrieved March 18, 2025.
  85. ^ "2025 antivirus market report: trends, stats and forecasts". Cybernews. April 22, 2025. Retrieved May 14, 2025.
  86. ^ Lv, Mingqi; Zeng, Huan; Chen, Tieming; Zhu, Tiantian (October 1, 2023). "CTIMD: Cyber Threat Intelligence Enhanced Malware Detection Using API Call Sequences with Parameters". Computers & Security. 136 103518. doi:10.1016/j.cose.2023.103518. ISSN 0167-4048.
  87. ^ Sandboxing Protects Endpoints | Stay Ahead Of Zero Day Threats Archived April 2, 2015, at the Wayback Machine. Enterprise.comodo.com (June 20, 2014). Retrieved on 2017-01-03.
  88. ^ Szor 2005, pp. 474–481.
  89. ^ Firdausi, Ivan; Lim, Charles; Erwin, Alva; Nugroho, Anto Satriyo (2010). "Analysis of Machine learning Techniques Used in Behavior-Based Malware Detection". 2010 Second International Conference on Advances in Computing, Control, and Telecommunication Technologies. p. 201. doi:10.1109/ACT.2010.33. ISBN 978-1-4244-8746-2. S2CID 18522498.
  90. ^ Schultz, M.G.; Eskin, E.; Zadok, F.; Stolfo, S.J. (2001). "Data mining methods for detection of new malicious executables". Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001. p. 38. CiteSeerX 10.1.1.408.5676. doi:10.1109/SECPRI.2001.924286. ISBN 978-0-7695-1046-0. S2CID 21791.
  91. ^ Shabtai, Asaf; Kanonov, Uri; Elovici, Yuval; Glezer, Chanan; Weiss, Yael (2011). ""Andromaly": A behavioral malware detection framework for android devices". Journal of Intelligent Information Systems. 38: 161. doi:10.1007/s10844-010-0148-x. S2CID 6993130.
  92. ^ Fox-Brewster, Thomas. "Netflix Is Dumping Anti-Virus, Presages Death Of An Industry". Forbes. Archived from the original on September 6, 2015. Retrieved September 4, 2015.
  93. ^ Automatic Malware Signature Generation Archived January 24, 2021, at the Wayback Machine. (PDF) . Retrieved on January 3, 2017.
  94. ^ Szor 2005, pp. 252–288.
  95. ^ "Generic detection". Kaspersky. Archived from the original on December 3, 2013. Retrieved July 11, 2013.
  96. ^ Symantec Corporation (February 2009). "Trojan.Vundo". Archived from the original on April 9, 2009. Retrieved April 14, 2009.
  97. ^ Symantec Corporation (February 2007). "Trojan.Vundo.B". Archived from the original on April 27, 2009. Retrieved April 14, 2009.
  98. ^ "Antivirus Research and Detection Techniques". ExtremeTech. Archived from the original on February 27, 2009. Retrieved February 24, 2009.
  99. ^ "Terminology – F-Secure Labs". Archived from the original on August 24, 2010.
  100. ^ "Real-Time Protection". support.kaspersky.com. Retrieved April 9, 2021.
  101. ^ "Kaspersky Cyber Security Solutions for Home & Business | Kaspersky". usa.kaspersky.com. Archived from the original on March 12, 2006.
  102. ^ Arp, D., Quiring, E., Pendlebury, F., Warnecke, A., Pierazzi, F., Wressnegger, C., Cavallaro, L., & Rieck, K. (2022). Dos and Don’ts of Machine Learning in Computer Security. In Proceedings of the 31st USENIX Security Symposium (USENIX Security 2022) (pp. 3971–3988). USENIX Association. https://doi.org/10.48550/arXiv.2010.09470​
  103. ^ Ceschin, F., Botacin, M., Bifet, A., Pfahringer, B., Oliveira, L. S., Gomes, H. M., & Grégio, A. (2023). Machine Learning (In) Security: A Stream of Problems. Digital Threats, arXiv:2010.16045. https://doi.org/10.48550/arXiv.2010.16045
  104. ^ Ceschin, F., Botacin, M., Gomes, H. M., Oliveira, L. S., & Grégio, A. (2019). Shallow Security: On the Creation of Adversarial Variants to Evade Machine Learning-Based Malware Detectors. In Proceedings of the 3rd Reversing and Offensive-Oriented Trends Symposium (ROOTS 2019). Association for Computing Machinery. https://doi.org/10.1145/3375894.3375898
  105. ^ Vasan, D., Alazab, M., Wassan, S., Naif, A., Safaei, B., & Zheng, Q. (2020). Malware Detection on Highly Imbalanced Data through Sequence Modeling. In Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security (AsiaCCS ’19) (pp. 195–206). Association for Computing Machinery. https://doi.org/10.1145/3338501.3357374
  106. ^ Kelly, Michael (October 2006). "Buying Dangerously". Archived from the original on July 15, 2010. Retrieved November 29, 2009.
  107. ^ Bitdefender (2009). "Automatic Renewal". Archived from the original on October 6, 2009. Retrieved November 29, 2009.
  108. ^ Symantec (2014). "Norton Automatic Renewal Service FAQ". Archived from the original on April 13, 2014. Retrieved April 9, 2014.
  109. ^ SpywareWarrior (2007). "Rogue/Suspect Anti-Spyware Products & Web Sites". Retrieved November 29, 2009.
  110. ^ Protalinski, Emil (November 11, 2008). "AVG incorrectly flags user32.dll in Windows XP SP2/SP3". Ars Technica. Archived from the original on April 30, 2011. Retrieved February 24, 2011.
  111. ^ "McAfee to compensate businesses for buggy update". Archived from the original on September 4, 2010. Retrieved December 2, 2010.
  112. ^ "Buggy McAfee update whacks Windows XP PCs". CNET. Archived from the original on January 13, 2011. Retrieved December 2, 2010.
  113. ^ Tan, Aaron (May 24, 2007). "Flawed Symantec update cripples Chinese PCs". CNET Networks. Archived from the original on April 26, 2011. Retrieved April 5, 2009.
  114. ^ a b Harris, David (June 29, 2009). "January 2010 – Pegasus Mail v4.52 Release". Pegasus Mail. Archived from the original on May 28, 2010. Retrieved May 21, 2010.
  115. ^ "McAfee DAT 5958 Update Issues". April 21, 2010. Archived from the original on April 24, 2010. Retrieved April 22, 2010.
  116. ^ "Botched McAfee update shutting down corporate XP machines worldwide". April 21, 2010. Archived from the original on April 22, 2010. Retrieved April 22, 2010.
  117. ^ Leyden, John (December 2, 2010). "Horror AVG update ballsup bricks Windows 7". The Register. Archived from the original on December 5, 2010. Retrieved December 2, 2010.
  118. ^ MSE false positive detection forces Google to update Chrome, October 3, 2011, archived from the original on October 4, 2011, retrieved October 3, 2011
  119. ^ Sophos Antivirus Detects Itself as Malware, Deletes Key Binaries, The Next Web, September 20, 2012, archived from the original on January 17, 2014, retrieved March 5, 2014
  120. ^ Shh/Updater-B false positive by Sophos anti-virus products, Sophos, September 19, 2012, archived from the original on April 21, 2014, retrieved March 5, 2014
  121. ^ If Google Play Protect is breaking bluetooth on your Moto G4 Plus, don't worry because there's a fix, Android Police, September 11, 2017, archived from the original on November 7, 2017, retrieved November 1, 2017
  122. ^ Windows Defender is reporting a false-positive threat 'Behavior:Win32/Hive.ZY'; it's nothing to be worried about, Windows Central, September 5, 2022, archived from the original on September 5, 2022, retrieved September 5, 2012
  123. ^ "Plus! 98: How to Remove McAfee VirusScan". Microsoft. January 2007. Archived from the original on April 8, 2010. Retrieved September 27, 2014.
  124. ^ Vamosi, Robert (May 28, 2009). "G-Data Internet Security 2010". PC World. Archived from the original on February 11, 2011. Retrieved February 24, 2011.
  125. ^ Higgins, Kelly Jackson (May 5, 2010). "New Microsoft Forefront Software Runs Five Antivirus Vendors' Engines". Darkreading. Archived from the original on May 12, 2010. Retrieved February 24, 2011.
  126. ^ "Steps to take before you install Windows XP Service Pack 3". Microsoft. April 2009. Archived from the original on December 8, 2009. Retrieved November 29, 2009.
  127. ^ "Upgrading from Windows Vista to Windows 7". Archived from the original on November 30, 2011. Retrieved March 24, 2012. Mentioned within "Before you begin".
  128. ^ "Upgrading to Microsoft Windows Vista recommended steps". Archived from the original on March 8, 2012. Retrieved March 24, 2012.
  129. ^ "How to troubleshoot problems during installation when you upgrade from Windows 98 or Windows Millennium Edition to Windows XP". May 7, 2007. Archived from the original on March 9, 2012. Retrieved March 24, 2012. Mentioned within "General troubleshooting".
  130. ^ "BT Home Hub Firmware Upgrade Procedure". Archived from the original on May 12, 2011. Retrieved March 6, 2011.
  131. ^ "Troubleshooting". Retrieved February 17, 2011.
  132. ^ "Spyware, Adware, and Viruses Interfering with Steam". Archived from the original on July 1, 2013. Retrieved April 11, 2013. Steam support page.
  133. ^ "Field Notice: FN – 63204 – Cisco Clean Access has Interoperability issue with Symantec Anti-virus – delays Agent start-up". Archived from the original on September 24, 2009.
  134. ^ Goodin, Dan (December 21, 2007). "Anti-virus protection gets worse". Channel Register. Archived from the original on May 11, 2011. Retrieved February 24, 2011.
  135. ^ "ZeuS Tracker :: Home". Archived from the original on November 3, 2010.
  136. ^ Illett, Dan (July 13, 2007). "Hacking poses threats to business". Computer Weekly. Archived from the original on January 12, 2010. Retrieved November 15, 2009.
  137. ^ Espiner, Tom (June 30, 2008). "Trend Micro: Antivirus industry lied for 20 years". ZDNet. Archived from the original on October 6, 2014. Retrieved September 27, 2014.
  138. ^ AV Comparatives (December 2013). "Whole Product Dynamic "Real World" Production Test" (PDF). Archived (PDF) from the original on January 2, 2014. Retrieved January 2, 2014.
  139. ^ Kirk, Jeremy (June 14, 2010). "Guidelines released for antivirus software tests". Computerworld. Archived from the original on April 22, 2011.
  140. ^ Harley, David (2011). AVIEN Malware Defense Guide for the Enterprise. Elsevier. p. 487. ISBN 978-0-08-055866-0. Archived from the original on January 3, 2014.
  141. ^ Kotadia, Munir (July 2006). "Why popular antivirus apps 'do not work'". ZDNet. Archived from the original on April 30, 2011. Retrieved April 14, 2010.
  142. ^ a b The Canadian Press (April 2010). "Internet scam uses adult game to extort cash". CBC News. Archived from the original on April 18, 2010. Retrieved April 17, 2010.
  143. ^ "Researchers up evilness ante with GPU-assisted malware". The Register. Archived from the original on August 10, 2017.
  144. ^ Iresh, Gina (April 10, 2010). "Review of Bitdefender Antivirus Security Software 2017 edition". www.digitalgrog.com.au. Digital Grog. Archived from the original on November 21, 2016. Retrieved November 20, 2016.
  145. ^ "Why F-PROT Antivirus fails to disinfect the virus on my computer?". Archived from the original on September 17, 2015. Retrieved August 20, 2015.
  146. ^ "Actions to be performed on infected objects". Archived from the original on August 9, 2015. Retrieved August 20, 2015.
  147. ^ "Cryptolocker Ransomware: What You Need To Know". October 8, 2013. Archived from the original on February 9, 2014. Retrieved March 28, 2014.
  148. ^ "How Anti-Virus Software Works". Archived from the original on March 2, 2011. Retrieved February 16, 2011.
  149. ^ "The 10 faces of computer malware". July 17, 2009. Archived from the original on February 9, 2011. Retrieved March 6, 2011.
  150. ^ "New BIOS Virus Withstands HDD Wipes". Tom's Hardware. March 27, 2009. Archived from the original on April 1, 2011. Retrieved March 6, 2011.
  151. ^ "Phrack Inc. Persistent BIOS Infection". June 1, 2009. Archived from the original on April 30, 2011. Retrieved March 6, 2011.
  152. ^ "Turning USB peripherals into BadUSB". Archived from the original on April 18, 2016. Retrieved October 11, 2014.
  153. ^ Greenberg, Andy (July 31, 2014). "Why the Security of USB Is Fundamentally Broken". Wired. Archived from the original on August 3, 2014. Retrieved October 11, 2014.
  154. ^ "How Antivirus Software Can Slow Down Your Computer". Support.com Blog. Archived from the original on September 29, 2012. Retrieved July 26, 2010.
  155. ^ "Softpedia Exclusive Interview: Avira 10". Ionut Ilascu. Softpedia. April 14, 2010. Archived from the original on August 26, 2011. Retrieved September 11, 2011.
  156. ^ "Norton AntiVirus ignores malicious WMI instructions". Munir Kotadia. CBS Interactive. October 21, 2004. Archived from the original on September 12, 2009. Retrieved April 5, 2009.
  157. ^ "NSA and GCHQ attacked antivirus software so that they could spy on people, leaks indicate". June 24, 2015. Retrieved October 30, 2016.
  158. ^ a b "Popular security software came under relentless NSA and GCHQ attacks". Andrew Fishman, Morgan Marquis-Boire. June 22, 2015. Archived from the original on October 31, 2016. Retrieved October 30, 2016.
  159. ^ Zeltser, Lenny (October 2010). "What Is Cloud Anti-Virus and How Does It Work?". Archived from the original on October 10, 2010. Retrieved October 26, 2010.
  160. ^ Erickson, Jon (August 6, 2008). "Antivirus Software Heads for the Clouds". Information Week. Archived from the original on April 26, 2011. Retrieved February 24, 2010.
  161. ^ "Comodo Cloud Antivirus released". wikipost.org. Archived from the original on May 17, 2016. Retrieved May 30, 2016.
  162. ^ "Comodo Cloud Antivirus User Guideline PDF" (PDF). help.comodo.com. Archived (PDF) from the original on June 4, 2016. Retrieved May 30, 2016.
  163. ^ Krebs, Brian (March 9, 2007). "Online Anti-Virus Scans: A Free Second Opinion". The Washington Post. Archived from the original on April 22, 2011. Retrieved February 24, 2011.
  164. ^ Cloudmersive. "CDR API". Cloudmersive. Retrieved October 10, 2025.
  165. ^ "Windows Malicious Software Removal Tool 64-bit". Microsoft. Retrieved December 27, 2022.
  166. ^ "Download Kaspersky Virus Removal Tool application". Kaspersky Lab. Retrieved December 27, 2022.
  167. ^ "Sophos Scan & Clean". Sophos. Retrieved December 27, 2022.
  168. ^ "How To Tell If a Virus Is Actually a False Positive". How To Geek. January 20, 2014. Retrieved October 2, 2018.
  169. ^ "Download Kaspersky Rescue Disk". Kaspersky Lab. Retrieved December 27, 2022.
  170. ^ "Rescue Disk". Trend Micro. Retrieved December 27, 2022.
  171. ^ "Best Comodo Rescue Disk 2022". Comodo Group. Retrieved December 27, 2022.
  172. ^ "FBI estimates major companies lose $12m annually from viruses". January 30, 2007. Archived from the original on July 24, 2012. Retrieved February 20, 2011.
  173. ^ Kaiser, Michael (April 17, 2009). "Small and Medium Size Businesses are Vulnerable". National Cyber Security Alliance. Archived from the original on September 17, 2012. Retrieved February 24, 2011.
  174. ^ Nearly 50% Women Don't Use Anti-virus Software Archived May 13, 2013, at the Wayback Machine. Spamfighter.com (September 2, 2010). Retrieved on January 3, 2017.

General bibliography

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
Antivirus software is a class of programs designed to detect, prevent, and remove malicious software—such as viruses, worms, trojans, , and other —by scanning files, memory, and system processes for known threat signatures, patterns, or behavioral anomalies indicative of . These tools originated in the early amid the emergence of self-replicating computer viruses like the and , evolving from basic scanners to comprehensive endpoint protection suites incorporating real-time monitoring, automatic updates, and integration with firewalls and intrusion detection. Despite widespread adoption on billions of devices, antivirus software operates reactively, relying on databases of identified threats that lag behind rapidly mutating or zero-day , rendering it insufficient as a standalone defense against sophisticated attacks. Independent laboratory evaluations, such as those conducted by and AV-Comparatives, demonstrate that leading commercial products achieve detection rates exceeding 99% for established samples in controlled tests from 2023 to 2025, with top performers like and Norton earning near-perfect scores across protection, performance, and usability metrics. However, these benchmarks highlight persistent limitations: signature-based methods fail against novel variants, while and approaches introduce false positives that legitimate files, eroding user trust and productivity. Resource-intensive scanning often degrades system performance, particularly on lower-end hardware, and the ongoing "" with authors underscores that no solution provides absolute protection, necessitating layered defenses like application whitelisting and user education. Controversies surrounding antivirus software include privacy risks from cloud-based scanning that transmits data to remote servers, potential vulnerabilities in the software itself exploited by attackers, and the inefficacy of free or bundled versions that prioritize upsells over robust protection. Empirical data from security researchers emphasizes that while antivirus mitigates common threats effectively in aggregate—averting widespread outbreaks of known malware—its causal impact diminishes against targeted, polymorphic, or fileless attacks, prompting a shift toward proactive measures like endpoint detection and response (EDR) systems in enterprise environments.

Historical Development

Pre-Antivirus Era and Initial Threats (1971–1980)

In 1971, Bob Thomas, an engineer at BBN Technologies, created Creeper, the first experimental self-replicating program, which spread across the ARPANET—a precursor to the modern internet connecting research institutions via DEC PDP-10 computers running the TENEX operating system. Creeper propagated by copying itself between nodes, displaying the message "I'm the creeper, catch me if you can!" on infected terminals, but it caused no data corruption or system crashes, serving primarily as a proof-of-concept for mobile code in networked environments. The program's replication demonstrated early risks of uncontrolled code mobility, as it leveraged ARPANET's resource-sharing protocols to hop between approximately 20-30 connected machines without user intervention. To mitigate Creeper's spread, , a colleague at BBN, developed , a companion program that actively searched for and deleted Creeper instances across the network, functioning as an rudimentary countermeasure through systematic scanning and erasure. operated on similar principles of mobility but targeted destructive removal rather than replication, highlighting the need for proactive hunting mechanisms in response to self-propagating threats. This manual intervention succeeded in containing Creeper without broader disruption, as ARPANET's scale remained limited to government and academic users. The lacked dedicated antivirus tools or commercial defenses, with responses confined to ad-hoc , physical isolation of infected hardware, and custom scripts written by system administrators familiar with the underlying assembly code and network protocols. No standardized detection methods existed, forcing reliance on direct code inspection and termination of processes, which proved feasible only due to the era's low connectivity and small user base—typically fewer than 100 nodes on by decade's end. Isolated incidents like the Wabbit program, a non-networking that exhausted resources by rapid self-duplication on single 1108 systems until crashes, underscored resource denial as another primitive threat but did not spur formalized protections beyond operator vigilance. These events established the empirical basis for as a vector for unintended propagation, setting the stage for later defensive innovations amid expanding computing access.

Dawn of Dedicated Antivirus Tools (1980–1990)

The release of the virus in January 1986 represented a pivotal catalyst for antivirus development, as it became the first virus specifically targeting IBM PC-compatible systems by infecting floppy disk boot sectors and displaying a message with the creators' contact information. Developed by Pakistani brothers Basit and Amjad Farooq Alvi to deter software piracy of their medical diagnostics program, Brain spread rapidly through shared disks, infecting over 10% of PCs in some regions by 1987 and heightening global awareness of self-replicating threats. This event shifted responses from manual, ad-hoc virus removal—such as rewriting infected code or reformatting disks—to the creation of dedicated scanning tools that systematically searched files for known malicious signatures. In 1987, founded Associates (later , Inc.) and released VirusScan, one of the earliest commercial antivirus programs for systems, which scanned memory, boot sectors, and files for predefined virus patterns derived from user-submitted samples. That same year, Germany's G Data Software AG introduced NOD, initially for Atari ST but soon adapted for PCs, focusing on boot sector detection. Additional tools emerged rapidly, including Ross Greenberg's Flushot Plus and Erwin Lanting's Anti4us, both released by late 1987, emphasizing real-time monitoring and basic file integrity checks over floppy-based infections. These programs marked the transition to purpose-built software, relying on databases of hexadecimal signatures to identify and quarantine threats, though updates required manual incorporation of new virus definitions. Eastern Europe's antivirus efforts, particularly in Bulgaria amid a surge of locally authored viruses like those from the "virus factory" scene, produced early freeware alternatives. Researcher Vesselin Bontchev, based in , developed and distributed initial free antivirus scanners in the late , analyzing and countering strains such as , Ping Pong, and Cascade through disassembly and extraction. These tools, often shared via systems, addressed regional threats exacerbated by limited Western software access under communist policies, fostering a approach to detection before commercial dominance. By , such programs had evolved to handle dozens of known es, but remained constrained by static methods that required frequent manual updates for emerging variants.

Commercialization and Industry Expansion (1990–2000)

Symantec released the first version of in December 1990, marking a significant step toward commercializing antivirus solutions for personal computers amid rising incidents like the Jerusalem variants. This product, initially a DOS-based scanner, evolved from Symantec's early development efforts starting in 1989 and catered to the growing consumer market by integrating virus detection with utilities from the acquired Computing. Concurrently, other firms entered the space, including Panda Software founded in 1990 in , which focused on user-friendly antivirus tools for systems. The professionalization accelerated with the formation of the Computer Antivirus Research Organization (CARO) in 1990, an international group of experts aimed at coordinating research, sharing threat intelligence, and standardizing definitions for . In 1993, antivirus researcher Joe Wells established the WildList, a monthly compilation of prevalent "in-the-wild" viruses reported by global experts, which served as a benchmark for testing and updating databases across vendors. These initiatives facilitated shared standardization, enabling more efficient detection as virus counts surged into the thousands by the mid-1990s, driven by the expansion of networked environments and usage. The outbreak of macro viruses, particularly the worm on March 26, 1999, which propagated rapidly via attachments and infected over 100,000 systems within days, underscored the need for proactive defenses. exploited Word macros to self-replicate and overload servers, causing widespread disruptions estimated at $80 million in damages. In response, antivirus firms rapidly updated s and introduced real-time monitoring features, such as on-access scanning and macro heuristics, to intercept threats before execution; , founded in 1997, exemplified this shift by emphasizing rapid deployment for emerging macro threats. This period's innovations, fueled by adoption, transformed antivirus from ad-hoc utilities into a competitive industry reliant on timely, collaborative threat intelligence.

Adaptation to Sophisticated Malware (2000–2010)

During the , antivirus software underwent significant adaptations to address the proliferation of sophisticated , such as polymorphic viruses that mutated to evade signature-based detection and stealthy rootkits that concealed malicious activities at the kernel level. Vendors increasingly incorporated advanced engines, which analyzed code for behavioral anomalies—like unusual file modifications or network calls—rather than relying solely on predefined signatures, enabling proactive identification of zero-day threats. This shift was driven by empirical observations of , where traditional methods failed against variants that altered their structure post-infection, as documented in analyses of second-generation threats. The rootkit incident in late 2005 exemplified these challenges, when approximately 20 million CDs distributed software that installed hidden components, rendering them undetectable by prevailing antivirus tools and exposing systems to secondary infections. In response, the cybersecurity community accelerated development of specialized rootkit detection mechanisms, including integrity checks on system files and boot processes; for instance, released RootkitRevealer in 2005, influencing subsequent integrations into commercial antivirus suites for hidden process enumeration and driver verification. These tools emphasized of persistence techniques, revealing how rootkits hooked system calls to mask presence, thereby prompting a broader industry pivot toward multi-layered scanning beyond user-mode heuristics. Industry consolidation intensified to pool resources against escalating threats, with Symantec Corp. acquiring Axent Technologies in July 2000 for nearly $1 billion in stock to enhance intrusion detection alongside its portfolio. Further bolstering capabilities, Symantec purchased Brightmail Inc. in 2004 for $370 million, integrating anti-spam heuristics that complemented filtering by examining message patterns for precursors to infections. Concurrently, subscription-based licensing models gained traction, supplanting one-time purchases to guarantee continuous and updates via cloud-delivered intelligence, as exemplified by Norton and McAfee's transitions that aligned revenue with the perpetual need for threat adaptation. The worm, emerging in November 2008, underscored persistent gaps in antivirus defenses, infecting an estimated 10 to 15 million Windows systems worldwide by exploiting the unpatched MS08-067 vulnerability while employing for command-and-control resilience. Its self-updating variants rapidly outpaced signature databases, forcing reliance on behavioral blocks for anomalous autorun behaviors, yet revealing how unpatched hosts and weak proactive heuristics allowed widespread propagation before coordinated takedowns. This event highlighted the causal limitations of isolated endpoint detection, as Conficker's dictionary-based password attacks and updates bypassed traditional perimeter controls, necessitating enhanced real-time monitoring in subsequent antivirus architectures.

Next-Generation Innovations and AI Integration (2010–present)

The integration of into antivirus solutions gained momentum around 2010 with the development of next-generation antivirus (NGAV), which prioritized behavior-based detection over traditional signatures to address zero-day threats. (EDR) technologies emerged prominently by 2013, enabling continuous endpoint monitoring, anomaly identification via algorithms, and automated incident response to counter advanced persistent threats in an intensifying attacker-defender . These predictive approaches marked an empirical shift, as evidenced by reduced reliance on reactive scanning and improved efficacy against polymorphic , though they introduced challenges in false positive management and computational overhead. Ransomware epidemics, exemplified by the 2017 WannaCry worm exploiting unpatched SMB vulnerabilities and affecting over 200,000 systems globally, accelerated adoption of behavioral blocking and engines. Vendors like deployed sandboxing and application control for preemptive blocking of WannaCry's propagation, while Symantec's Endpoint Protection proactively neutralized exploit attempts without signature updates. This response underscored causal links between unpatched systems and rapid lateral movement, prompting hybrid defenses combining local s with cloud-sourced intelligence for faster threat correlation. Market dynamics facilitated innovation through consolidation, including Avast's $1.3 billion acquisition of AVG in 2016 to expand behavioral analytics capabilities and NortonLifeLock's $8.6 billion purchase of in 2021, creating with enhanced resources for AI-driven R&D. Cloud-hybrid architectures proliferated, integrating on-device processing with remote for scalable sharing, as in Defender Antivirus's cloud-extended updates that deliver real-time protections across endpoints. AI enhancements in platforms like Defender evolved through 2025, incorporating advanced engines for behavioral pattern recognition and reduced latency in blocking novel attacks. Independent assessments affirm these advancements' impact; in AV-TEST's August 2025 evaluation of home products, Total Security earned top-product status with near-perfect aggregate scores (approaching 18/18 points) in protection against real-world and zero-day , performance impact, and usability. Such results highlight empirical gains in predictive detection, though efficacy remains contingent on timely updates and integration with broader security stacks amid evolving evasion tactics.

Core Technical Mechanisms

Signature-Based Detection

Signature-based detection operates by extracting unique identifiers, or signatures, from scanned files or code segments and matching them against a predefined database of known malware patterns. These signatures typically consist of cryptographic hashes, such as MD5 or SHA-256, computed from entire files or specific byte sequences characteristic of malicious software. For instance, during a scan, the antivirus software generates a hash value for a target file and queries its signature database; an exact match triggers an alert or quarantine action, ensuring deterministic identification of threats previously cataloged by the vendor. While MD5 hashes were commonly employed in early implementations, their vulnerability to collision attacks has led to a preference for more secure algorithms like SHA-256 in modern systems. The signature databases, often comprising millions of entries derived from malware samples analyzed in vendor laboratories, are distributed through regular update feeds to endpoint protection clients. This method excels in accuracy for established threats, as the pattern-matching process yields precise detections with minimal erroneous positives when signatures are well-curated. Vendors maintain these libraries by reverse-engineering submitted samples, extracting invariant code features, and generating new signatures for dissemination, enabling rapid coverage of prevalent families once identified in the wild. To address evasion tactics employed by malware authors, such as through , packing, or minor code alterations that modify hash values without altering core functionality, signature databases require frequent updates—typically several times per day—to incorporate signatures for newly obfuscated variants. Without such timely refreshes, detection efficacy diminishes against polymorphic or repacked that evades initial hashing by presenting altered fingerprints. This update dependency underscores the method's reliance on proactive threat intelligence gathering to sustain its foundational role in antivirus scanning.

Heuristic and Behavioral Analysis

Heuristic analysis employs rule-based algorithms to detect unknown by identifying code structures and patterns that resemble known threats, independent of static signatures. These rules evaluate attributes such as obfuscated code segments, anomalous invocations, or self-modifying routines that suggest malicious intent. For instance, a program attempting to overwrite files in directories or replicate across drives may trigger detection through weighted scoring of suspicious traits. This method emulates by prioritizing behaviors empirically linked to malware propagation, such as evasion of checks, though it risks elevated false positives from benign anomalies. Behavioral analysis complements heuristics through dynamic monitoring of process execution, tracking runtime activities like file system alterations, registry manipulations, or outbound communications that deviate from established baselines. Antivirus engines flag sequences such as a newly launched injecting into browser executables or establishing persistent connections to unfamiliar domains, intercepting threats before full deployment. This proactive stance relies on observing execution flows to infer harm, distinct from pre-scan . A core implementation involves sandboxing, where potentially harmful files are detonated in emulated environments to capture behavioral artifacts, including memory access patterns or privilege escalations, without compromising the production system. Such isolation enables detailed logging of causal chains, like encryption routines targeting user data, facilitating verdict on containment. Heuristic and behavioral methods thus address variant proliferation by focusing on operational invariants rather than immutable identifiers.

Machine Learning and AI-Driven Methods

Machine learning and AI-driven methods in antivirus software employ statistical models trained on extensive datasets of known and benign files to predict and classify potential threats through rather than rigid rule-matching. These approaches analyze features such as structures, calls, and behavioral traces to generate probabilistic scores for maliciousness, allowing detection of variants that evade traditional signatures. Training typically involves corpora with millions of samples; the dataset, for example, provides over 1.1 million portable executable files labeled for malicious and benign classification, enabling for . Subsequent iterations like 2024 expand to 3.2 million files across multiple formats, incorporating evasive to improve model generalization against obfuscated threats. Neural networks, including convolutional and recurrent variants, process these inputs to model complex attack patterns, such as polymorphic code changes, facilitating zero-day identification by flagging deviations from learned norms. In practice, deep learning classifiers achieve detection rates exceeding 95% for unseen samples in controlled evaluations, as demonstrated by ResNet50-based models on binary fragments. Commercial implementations, such as those in Kaspersky and products, leverage cloud-based ML to refine models in real-time against emerging vectors, prioritizing causal indicators like sequences over superficial heuristics. Norton integrates machine learning into its scanning engines for emulation-based behavior prediction and scam filtering, correlating with strong performance in independent benchmarks. AV-Comparatives' September 2025 false alarm test reported low erroneous detections for AI-enhanced products, with some achieving zero false positives across thousands of clean files, underscoring improved precision from data-driven tuning. However, hinges on dataset diversity and adversarial robustness; biased or static sets risk missing adversarial perturbations that mimic benign traits, as evidenced in studies on evasion against neural classifiers.

Specialized Detection Techniques

Specialized detection techniques in antivirus software address deeply embedded threats like and , which evade conventional signature-based or behavioral scans by operating at low system levels. detection relies on kernel-level integrity checks that compare system binaries and modules against known good hashes or digital signatures to identify unauthorized modifications. For instance, (PKI)-based validation ensures boot- and kernel-level components remain untampered, as alterations by would fail signature verification. Boot-time analysis complements this by scanning during system initialization, targeting in the or early kernel stages before they can hide processes or files. Real-time file and system hooks provide on-access prevention by integrating antivirus agents into the operating system's I/O subsystem, intercepting read, write, or execute operations at the kernel level. This allows immediate scanning of files prior to access, blocking potential execution without relying on periodic full-system scans; for example, Windows antivirus solutions register callbacks with the mini-filter to monitor all I/O requests. Such hooks enable proactive defense against zero-day threats by enforcing real-time enforcement, though they introduce measurable latency in file operations. Firmware scanning targets persistent infections in BIOS or UEFI, which reside outside the OS and survive reboots or reinstalls; these scans access SPI flash memory to detect anomalies in boot code or modules. Antivirus implementations, such as those in Microsoft Defender for Endpoint, analyze UEFI signals during runtime or boot for unauthorized code execution, while tools like ESET's UEFI Scanner perform on-demand integrity verification of firmware sectors. Though firmware threats remain rare— with documented cases like the 2015 Hacking Team UEFI rootkit— their detection requires hardware-level access and specialized drivers, underscoring the technique's niche role in comprehensive threat mitigation.

Effectiveness and Empirical Assessment

Independent Testing Frameworks and Metrics

Independent testing frameworks for antivirus software rely on standardized protocols to evaluate efficacy under controlled conditions that mimic real-world threats, providing empirical benchmarks for protection, system impact, and reliability. Prominent organizations include , which conducts comparative tests across Windows, macOS, and Android platforms, assessing products against thousands of malware samples monthly. AV-Comparatives performs real-world protection tests simulating dynamic threat encounters, including online and offline scenarios. Other labs such as SE Labs employ hacker-like simulations using actual attack vectors, while MRG Effitas focuses on 360° assessments emphasizing financial and prevention. These frameworks adhere to guidelines from the Anti- Testing (AMTSO) to ensure methodological consistency and transparency in test design. Core metrics center on protection rates, quantifying the percentage of threats blocked or detected, often segmented by prevalence (e.g., widespread vs. zero-day ) and type (e.g., subsets). AV-TEST's protection module tests real-time defense against 0-day attacks via web and vectors, alongside retrospective scans for known threats, with scores derived from detection efficacy. AV-Comparatives' Malware Protection Test measures proactive blocking before execution, reactive detection during, and post-infection removal, prioritizing causal prevention over mere identification. SE Labs incorporates framework stages to evaluate (EDR) across attack chains, yielding accuracy ratings that balance threat neutralization against legitimate file handling. Performance impact metrics assess resource overhead through benchmarks like application launches, file operations, and archiving under active scanning. AV-Comparatives simulates everyday tasks to score slowdowns, while uses realistic workloads to measure CPU, memory, and disk effects. Usability scores evaluate false positive rates by exposing products to files and benign URLs, ensuring low disruption to legitimate activities; high false alarms can indicate overzealous heuristics that erode practical value. MRG Effitas integrates these into certifications requiring near-perfect scores (e.g., 99%+) across and modules for validation. These metrics collectively prioritize causal threat mitigation, with zero-day and emphases reflecting evolving attack surfaces where signature delays prove insufficient.

Detection Performance Data from Recent Evaluations

In the February–May 2025 Real-World Protection Test conducted by AV-Comparatives, 19 antivirus products were evaluated against 423 live internet threats on , simulating and encounters. achieved the highest protection rate of 99.8%, blocking 422 out of 423 cases, while products like , G Data, , Norton, , and VIPRE scored 99.5%. Lower performers included at 94.3%. rates varied, with Total Defense reporting zero and 52, averaging 11 across products; excessive false positives led to downgrades for several, including AVG, , and Panda.
ProductBlockedCompromisedProtection Rate
422199.8%
Avast, G Data, , Norton, , VIPRE421299.5%
AVG, 420399.3%
, 419499.1%
3992494.3%
The September 2025 Malware Protection Test by AV-Comparatives assessed offline and online detection against a large set. Online detection rates for top products reached 100%, including , G Data, , and Defender, with others like , , , Kaspersky, TotalAV, and Total Defense at 99.99%. Offline rates were lower, topping at 99.1% for G Data and 98.8% for , Total Defense, and VIPRE, reflecting challenges with signature-independent polymorphic variants. False positives were minimal for Kaspersky (3) and higher for and (45 each). AV-TEST's August 2025 evaluation of 13 Windows home products emphasized protection against prevalent and zero-day , awarding top scores (6/6 points, correlating to 99–100% detection) to leading suites like and Kaspersky, with usability metrics indicating low false positives under default settings. Longitudinal data from these labs show detection rates for top commercial antivirus improving to near-perfect in online and behavioral scenarios due to AI integration, yet gaps persist in offline polymorphic detection, where rates hover 2–6% below online equivalents.

Factors Influencing Real-World Efficacy

The efficacy of antivirus software in real-world environments diverges from controlled laboratory assessments due to dependencies on timely updates, which refresh signature databases and behavioral models to counter emerging threats. New malware variants proliferate daily, necessitating frequent updates—ideally daily—to maintain high detection rates, as delays can result in up to 10% variability in performance over observation periods. User compliance with these updates is a primary modulator; empirical analyses indicate that non-adoption or postponement, often driven by perceived inconvenience or risk aversion, exposes systems to unpatched vulnerabilities, undermining even robust detection engines. Integration with native operating system defenses further influences practical outcomes, particularly on Windows where provides baseline protection that third-party solutions must complement without conflict. In enterprise settings, combining antivirus with Defender for Endpoint enhances and response via cloud integration, yielding superior real-time blocking compared to standalone deployments. Independent evaluations confirm that properly configured synergies reduce evasion risks, though misconfigurations—such as automatic disabling of Defender by incompatible third-party tools—can degrade overall efficacy by fragmenting layered defenses. User-perceived effectiveness often exceeds empirical breach realities, with 2025 surveys reporting that 75% of respondents view antivirus as highly protective against infections. However, real-world incident data reveals persistent gaps, as malware persists in penetrating protected endpoints due to behavioral adaptations and incomplete scanning coverage, with studies documenting detection shortfalls in dynamic environments despite high lab scores. This discrepancy underscores causal factors like endpoint heterogeneity and user-induced delays in scans, which dilute signature and heuristic reliability beyond idealized test conditions.

Limitations and Criticisms

Evasion Techniques and Zero-Day Vulnerabilities

Malware employs polymorphism to evade signature-based detection by dynamically altering its code structure while preserving core functionality, generating unique variants that do not match predefined antivirus signatures. This technique involves mutating non-essential code sections, such as inserting junk instructions or reordering operations, rendering static pattern matching ineffective as each infection instance presents a novel hash. Empirical analysis of polymorphic samples demonstrates that such mutations can bypass up to 90% of signature-dependent scanners until behavioral heuristics are updated, exploiting the causal gap between fixed detection rules and adaptive attacker code generation. Packing and further obscure malware payloads, compressing executables with tools like or custom crypters to hide original code from scanners, which often fail to unpack and analyze dynamically during initial execution. Packers wrap the malicious binary in layers that decrypt only at runtime, evading static since antivirus engines typically inspect unpacked forms post-decryption, a attackers delay through anti-analysis hooks. When combined with encryption, where payloads are ciphered using algorithms like AES and decrypted via embedded keys, these methods causally disrupt heuristic engines reliant on visible behavioral indicators, as encrypted code appears benign until activation. Studies of real-world campaigns, such as those using custom packers in , confirm evasion rates exceeding 70% against legacy antivirus without runtime unpacking capabilities. Zero-day vulnerabilities represent exploits of undisclosed software flaws, inherently bypassing antivirus defenses that depend on prior of threats, with empirical indicating an average exploit lifespan of 312 days before patches or signatures emerge. Attackers leverage these gaps in unpatched systems, such as browser or kernel weaknesses, to deliver payloads undetected, as no behavioral baseline exists for attack vectors like . In advanced persistent threats (APTs), antivirus often misses initial footholds, with assessments showing endpoint solutions failing to block 40-60% of simulated APT stages due to reliance on reactive updates rather than proactive . In rare cases, malware can compromise antivirus software by exploiting vulnerabilities in the AV product for privilege escalation, such as flaws in drivers, parsers, or installers that allow attackers to gain elevated system access. Past reports across major vendors document these exploits, but they are typically patched rapidly upon disclosure. Advanced malware like rootkits can theoretically modify or disable antivirus operations by interfering with kernel callbacks or hiding components, with limited real-world examples such as the "Spicy Hot Pot" rootkit; however, such direct compromises remain extremely uncommon in typical user environments. Risk homeostasis theory posits that antivirus deployment can induce user complacency, where perceived protection lowers vigilance, causally offsetting safety gains by encouraging riskier behaviors like clicking unverified links or delaying updates. Empirical tests in controlled environments reveal that users with active antivirus exhibit 20-30% higher exposure to simulations, compensating for the tool's presence by reducing other precautions, thus maintaining baseline risk levels. This dynamic underscores a fundamental limitation: while antivirus mitigates known threats, it may amplify overall through behavioral , as attackers exploit human overreliance on automated defenses.

Performance Overhead and Resource Consumption

Antivirus software imposes varying degrees of performance overhead depending on scanning mode and intensity, with real-time protection generally exhibiting lower continuous impact than on-demand full scans. In real-time modes, CPU utilization typically ranges from 5-15% during background operations on modern hardware, with spikes up to 25-30% during file access or updates, as observed in Microsoft Defender deployments. RAM consumption for active real-time scanning averages 100-300 MB across lightweight to full-featured products, enabling efficient matching without excessive pressure. Full system scans represent the highest resource demands, often extending task durations by 3-10% in operations like file copying, archiving, and application launching, according to AV-Comparatives evaluations on Windows systems with i7 processors and 8 GB RAM. In more intensive benchmarks, certain products have demonstrated slowdowns reaching 29% during full scans, particularly those involving heavy disk I/O that can conflict with concurrent software activities like browsing or multitasking. These impacts are mitigated in optimized products scoring highly in UL benchmarks, where scores near 97-100 indicate near-baseline system speeds. On mobile devices, antivirus applications contribute to battery drain primarily through continuous monitoring, with certified solutions limited to under 8% additional consumption in standardized tests simulating real-world usage. Full scans or intensive heuristics exacerbate this, potentially accelerating battery wear via sustained CPU and network activity, though modern Android optimizations keep idle impacts negligible for top performers. To balance protection and efficiency, many vendors employ cloud offloading, where suspicious files are analyzed remotely to reduce local CPU and RAM loads by up to several-fold during peak scanning. This approach trades reduced on-device computation for added network latency, typically 100-500 ms per offloaded query, which can delay real-time responses in low-bandwidth scenarios. Such techniques highlight inherent trade-offs, as heightened scanning rigor inversely correlates with system fluidity absent hardware accelerations like SSDs or multi-core processors.

False Sense of Security and Risk Homeostasis

Antivirus software often engenders a false sense of security among users, who may perceive it as comprehensive protection against all threats, thereby underestimating residual risks. This psychological effect aligns with risk homeostasis theory, which posits that individuals calibrate their behavior to maintain a preferred level of risk, compensating for perceived safety gains by increasing exposure elsewhere. Originally developed in safety research, the theory has been adapted to cybersecurity, where protective measures like antivirus (AV) installation can inadvertently prompt riskier actions, such as visiting unverified websites or ignoring suspicious indicators, as users feel buffered from consequences. Empirical studies support this dynamic. A 2020 analysis of survey data from 1,072 respondents tested a revised homeostasis model specific to cybersecurity technologies, finding that AV users reported higher engagement in risky online behaviors compared to non-users, including downloading from untrusted sources and bypassing warnings, suggesting compensatory risk-taking that offsets protective benefits. Similarly, a 2023 investigation into user adoption of tools revealed that individuals equipped with such software were more likely to partake in hazardous activities—like clicking unsolicited links—and exhibited a positive between tool usage and actual rates, indicating that perceived fosters behavioral laxity rather than enhanced caution. These findings challenge assumptions of additive protection, highlighting how AV may stabilize or even elevate net through human . Data on breach incidents further underscores the pitfalls, with breaches persisting in environments deploying AV due to unaddressed human factors. Approximately 90% of cyber incidents stem from or behavior, such as susceptibility or poor judgment, which AV detects reactively but cannot preempt without vigilant user input. Organizational reports and analyses consistently show that even widespread AV deployment fails to eradicate threats when users, emboldened by software reliance, neglect foundational practices like verifying senders or updating habits, perpetuating a cycle where technical defenses mask behavioral vulnerabilities. Thus, no AV solution substitutes for proactive user awareness; overdependence risks amplifying exposure in ways empirical patterns confirm.

Key Controversies

False Positives and Collateral Damage

False positives occur when antivirus software incorrectly identifies legitimate files or programs as malicious, leading to their , deletion, or blockage. This error stems from detection methods that prioritize broad threat coverage, often flagging benign code patterns resembling malware signatures. Such incidents disproportionately affect legitimate operations, as users may lose access to essential tools, incurring operational disruptions. In 2009, developer Nir Sofer of NirSoft documented repeated false positives on utilities like password recovery tools, where antivirus vendors such as and NOD32 classified them as trojans despite no malicious intent. These detections prevented downloads from sites like MajorGeeks and , forcing users to delete functional software and overwhelming small developers with support requests. Small-scale creators, lacking the influence or resources of major firms, faced prolonged delays in fixes, as vendors required manual sample submissions that could take weeks, exacerbating release timelines for updates. Similar cases persist, as seen with tools like VirtualMIDISynth, where heuristics triggered alerts on unsigned executables common among indie developers. Recent independent evaluations underscore ongoing variability in false positive rates across vendors. In the AV-Comparatives False Alarm Test of 2025, which scanned over 1 million clean files, products ranged from 3 false alarms (Kaspersky) to 85 (Panda), with others like at 46 and at 45. This disparity highlights how aggressive tuning in some engines harms reliability, penalizing scores for exceeding 10 false positives and revealing systemic inconsistencies in distinguishing safe software. High false positive vendors impose greater , as quarantined files can halt development workflows or break enterprise deployments. The fallout extends to eroded user trust and resource burdens: developers divert time to whitelisting appeals, while end-users experience alert fatigue, potentially disabling protections or ignoring genuine threats. For small developers, these episodes compound costs through lost productivity and reputational harm, as blocked tools deter adoption without recourse to paid certification programs favoring larger entities. In severe cases, automated responses delete critical system-adjacent files, risking or instability until manual restoration.

Rogue Antivirus Scams

Rogue antivirus software, also known as , consists of fraudulent programs designed to mimic legitimate tools by displaying fabricated infection alerts, simulated scans, and urgent pop-up warnings that pressure users into purchasing nonexistent remediation services, typically priced between $30 and $80 per license. These tactics exploit fear through social , often hijacking browsers to alter homepages, inject fake advertisements, or trigger persistent notifications claiming severe system compromise, thereby bypassing rational user verification. Secureworks analyses from 2008 detailed how such software, exemplified by variants like Antivirus XP 2008, was distributed via affiliate networks that incentivized promoters with commissions up to 75% of sales, generating substantial illicit revenue through volume-driven deception. The scams proliferated in the late 2000s and early , with Symantec reporting widespread global distribution between July 2008 and June 2009, including variants that evaded initial detection by legitimate antivirus through polymorphic code changes and exploit kits. By October 2008, estimates indicated over 30 million users worldwide had been victimized, leading to financial losses and the installation of additional under the guise of protection. U.S. authorities, including the FBI, attributed at least $150 million in profits to these operations by December 2009, facilitated by black-market affiliate models and drive-by downloads from compromised legitimate ad networks. Kaspersky noted in 2009 that rogue antivirus represented a dominant , with clean systems falsely flagged to sustain the scam cycle. Empirical evidence underscores the limitations of legitimate antivirus in preemptively blocking rogue installations, as these programs often employ zero-day exploits, URL obfuscation, and user-initiated downloads that precede signature-based detection. A 2010 Google analysis found that fake antivirus accounted for 15% of malware downloads, highlighting how even deployed protections failed against socially engineered lures, with over 11,000 domains hosting such threats via deceptive advertising. This vulnerability persists because rogue software prioritizes evasion over payload aggression, allowing it to infiltrate systems before full heuristic analysis activates, thereby revealing gaps in real-time behavioral monitoring across vendors. While peak activity waned post-2010 due to improved browser sandboxes and actions, tactics have adapted into broader tech support frauds and precursors, maintaining the core model of fear-induced payments but shifting toward remote access scams.

Privacy Implications of Cloud and Behavioral Monitoring

-based scanning in antivirus software involves transmitting file hashes, metadata, or entire suspicious files from user devices to remote servers for advanced against vast databases, enabling real-time sharing across endpoints. This process, employed by vendors like Defender and Webroot, enhances detection of novel but inherently exposes users' data to third-party storage and processing, where vulnerabilities in vendor infrastructure could lead to unauthorized access. Empirical risks materialize through potential data leaks; for instance, antivirus providers maintain centralized repositories of scanned samples, which have been identified as attractive targets for nation-state seeking to evade detection or extract . Behavioral monitoring complements features by observing runtime processes, calls, and system modifications in real time to identify anomalous activities indicative of zero-day exploits or , as implemented in tools like . However, this entails continuous logging of user and application behaviors, often without granular options or clear disclosure of policies, paralleling broader mechanisms and eroding user autonomy over personal computing environments. analyses highlight that such tracking collects patterns potentially revealing sensitive habits, with minimal transparency in how aggregated behavioral data informs vendor models or third-party sharing. Government access amplifies these concerns, as revelations from leaks indicate intelligence agencies, including the NSA and , have targeted antivirus firms to insert backdoors or harvest submitted samples for offensive capabilities. While vendors assert compliance with legal warrants, the opacity of pipelines—coupled with features enabled by default during installation—limits , often burying opt-outs in end-user license agreements reviewed by few users. For non-expert users, the causal trade-off favors localized scanning where feasible, as historical targeting of antivirus databases underscores persistent vulnerabilities outweighing marginal detection gains in low-threat scenarios.

Complementary and Alternative Approaches

Operating System Built-in Protections

incorporate native security features designed to detect and mitigate threats without requiring third-party software, providing a lightweight baseline defense suitable for typical user activities such as web browsing, , and app usage. These built-in tools leverage signature-based detection, heuristics, and real-time monitoring integrated directly into the OS kernel or app ecosystems, minimizing resource overhead and compatibility issues that can arise from layered antivirus solutions. Independent testing indicates these protections often achieve detection rates comparable to premium products for common threats, supporting their adequacy for non-enterprise or low-risk scenarios. Microsoft Defender Antivirus, formerly Windows Defender, serves as the default in and 11, offering real-time scanning, cloud-assisted behavioral analysis, and automatic updates since its rebranding in 2020. In evaluations for January and February 2025, it achieved 100% against prevalent and zero-day samples, earning top-product status with minimal false positives. Similarly, AV-Comparatives' September 2025 malware test awarded it high scores for blocking threats while maintaining low system impact, outperforming some paid alternatives in balanced performance metrics. This integration reduces the necessity for supplementary antivirus, as third-party installations trigger Defender's passive mode, potentially leading to detection gaps or conflicts without proportional gains for casual users. On macOS, XProtect provides signature-based scanning that automatically checks downloaded files and apps against a database of known hashes, updated periodically via releases without user intervention. Introduced in and enhanced with behavioral heuristics in later , it blocks execution of detected threats at launch, focusing on , on-demand verification rather than constant full-system scans to preserve battery life and performance. Apple's documentation confirms its role in mitigating common macOS-targeted , though it primarily targets signatures of established variants rather than novel exploits. For average users avoiding high-risk behaviors, this suffices alongside Gatekeeper's app notarization, obviating heavier third-party tools that could interfere with Apple's stack. Android's Google Play Protect, enabled by default on devices with , employs on-device and cloud verification to scan installed apps for malicious behavior, achieving 98.9% detection of new variants in recent assessments. Updates in 2025 introduced pattern-based rules for faster family identification, contributing to billions of harmful app blocks annually. Like its counterparts, it integrates seamlessly with the OS to avoid redundancies, with studies affirming its effectiveness for standard mobile use, where risks are the primary vector—thus diminishing the value of additional scanners for most consumers. Empirical data from labs supports that relying on these native defenses correlates with low rates among cautious users, underscoring their role as a sufficient first line without the bloat of external antivirus.

Network-Level and Hardware Defenses

Next-generation firewalls (NGFWs) operate at the network perimeter to inspect and filter inbound , preventing propagation by enforcing stateful packet inspection, application control, and intrusion prevention. Unlike traditional firewalls, NGFWs incorporate threat intelligence feeds and sandboxing to identify zero-day exploits and encrypted threats, blocking them before endpoint exposure. Independent benchmarks demonstrate leading NGFW platforms achieving 99.8% prevention rates alongside 100% blockade. These capabilities extend to decrypting SSL/TLS for analysis, addressing the 12.91% of -transmitted network that employs as of 2023. Hardware defenses embedded in silicon, such as Trusted Platform Modules (TPMs), enable root-of-trust mechanisms that safeguard boot processes against firmware-level tampering. TPM 2.0, standardized since , stores cryptographic keys and performs platform configuration registers (PCRs) measurements to validate boot components from / onward, halting execution if anomalies indicative of rootkits are detected. Secure Boot, integrated with TPM attestation, restricts loading to cryptographically signed firmware and loaders, mitigating persistent threats that traditional host-based antivirus may overlook during runtime. documentation confirms TPM-facilitated boot integrity checks support anti-malware validation of OS start states, enhancing resistance to bootkit infections. Cloud-based antivirus services complement these by routing file scans and behavioral analysis to remote servers, minimizing resource demands on endpoints like mobile or IoT devices. Vendors such as TotalAV employ cloud engines for real-time unknown file interrogation, leveraging aggregated threat data for detection rates surpassing local-only scanning in dynamic environments. This offloading model proved effective in n-version ensemble approaches, where distributed cloud scanners reduced false negatives by cross-verifying signatures across engines. Deployment in low-compute scenarios, as reviewed in 2021 studies extended to IoT protections, yields scalable defense without supplanting perimeter hardware.

User Education and Behavioral Strategies

User education and behavioral strategies constitute the primary defense against by targeting human vulnerabilities that antivirus software inherently cannot address, such as susceptibility to social engineering tactics. Analyses of breach incidents reveal that non-malicious human actions, including victimization and configuration errors, factor into 68% of data breaches, often initiating vectors that evade detection tools. These findings highlight the causal role of user decisions in enabling malware entry, as technical signatures fail against novel deceptions relying on trust exploitation rather than code execution. Core practices include phishing recognition , which emphasizes scrutinizing sender authenticity, avoiding unsolicited links or attachments, and verifying requests through independent channels. Empirical evaluations of awareness programs show limited but measurable benefits, with simulated reducing phishing click rates by 2-3% in large-scale studies, though efficacy depends on ongoing reinforcement rather than one-off sessions. Promptly applying software patches mitigates known exploits, responsible for nearly 60% of compromises per victim surveys, while routine backups—stored offline or immutably—facilitate recovery, succeeding in 69% of cases among affected organizations with prepared systems. Adjunct tools like password managers bolster these habits by enforcing unique, complex credentials across accounts, thereby curbing credential-stuffing attacks; users without them face three times the risk compared to diligent adopters. Overall, these strategies prioritize proactive vigilance over passive reliance on software, as propagation fundamentally traces to behavioral lapses that no scanner can preempt without user agency.

Industry Landscape and Adoption

Major Vendors and Market Competition

and Norton stand out as leading commercial antivirus vendors, earning top ratings from independent labs for superior detection rates exceeding 99% and minimal system impact in 2025 tests. also delivers high efficacy in threat neutralization, often scoring perfectly in AV-Comparatives evaluations, though its Russian base has prompted U.S. federal bans on since 2017 and private sector hesitancy amid risks, limiting its Western market penetration. Defender dominates via native integration in Windows, offering robust endpoint protection that rivals paid alternatives in real-time scanning and exploit mitigation without requiring third-party installs. Competitive dynamics hinge on differentiation through multi-layered architectures—combining signature-based detection, machine learning heuristics, and sandboxing—alongside value-added features like integrated VPNs for privacy enhancement and password managers. Suites such as Norton 360 and Bitdefender Total Security lead 2025 rankings by bundling these elements into unified platforms, pressuring rivals to innovate beyond basic scanning to address sophisticated ransomware and zero-day exploits. This feature escalation reflects free-market incentives, where lab certifications and user benchmarks drive vendors to optimize for low false positives and cross-platform compatibility, including macOS and mobile defenses. Consolidation via acquisitions has reshaped the landscape, enabling scale for R&D investment; for instance, Gen Digital's ownership of Norton, , and AVG since 2022 facilitates shared threat intelligence feeds, though boutique players like thrive on specialized agility in . Such churn promotes specialization in niche areas like enterprise (EDR), countering risks while geopolitical barriers fragment global competition, favoring U.S.- and EU-based firms in regulated sectors.

Usage Statistics and Economic Scale

In the United States, approximately 121 million individuals rely on third-party antivirus software for personal device protection as of 2025, representing a substantial portion of users amid ongoing cyber threats. This adoption rate aligns with broader surveys indicating that around 85% of Americans employ some form of antivirus solution, driven by concerns over and data breaches. Globally, daily engagement with antivirus platforms exceeds 30 million users, underscoring widespread recognition of persistent digital risks despite built-in operating system defenses. The economic scale of the antivirus industry reflects sustained demand, with the global market valued at $4.13 billion in 2024 and projected to reach $4.19 billion in 2025. Longer-term forecasts anticipate growth to $9.18 billion by 2034, at a compound annual growth rate of 6.7%, fueled by escalating breach incidents that cost organizations an average of $4.88 million per event. In the U.S. alone, nearly 17 million non-users intend to adopt antivirus software within the next six months, signaling potential for further market expansion as ransomware and malware attacks rose 22% in 2024. Industry trends emphasize a shift toward subscription-based models, which now dominate pricing strategies over one-time purchases, enabling regular updates and scalable features for diverse user segments. User perceptions reinforce this viability, with 88% of viewing antivirus software as effective in mitigating threats, contributing to high retention and revenue stability. The antivirus software sector has experienced notable consolidation, driven by that integrate endpoint protection with broader cybersecurity platforms. For instance, completed its acquisition of in February 2025, enhancing its managed detection and response capabilities. Similarly, private equity firms including and competed to acquire in early 2025, signaling interest in consolidating antivirus expertise with enterprise security services. This activity, part of a resurgence in cybersecurity M&A fueled by , reduces the pool of standalone vendors and promotes bundled offerings, though it may limit specialized choices for users. Future developments emphasize AI and integration to shift from reactive signature detection to proactive behavioral analysis, countering AI-augmented threats like automated and generation. Zero-trust models are gaining traction in antivirus evolution, enforcing continuous authentication and least-privilege access at the endpoint level to mitigate insider and lateral movement risks, rather than relying on static perimeters. These integrations align with empirical needs for adaptive defenses, as traditional tools struggle against polymorphic attacks. Emerging challenges include the proliferation of IoT devices, projected to exceed 75 billion connections by 2030, which demand lightweight, distributed antivirus agents capable of real-time across heterogeneous networks. Quantum computing poses longer-term risks by threatening asymmetric encryption used in secure communications and data-at-rest protection, prompting antivirus vendors to incorporate post-quantum cryptographic algorithms in updates. Despite these shifts, core reliance on verifiable threat intelligence and user-configured policies persists, as no automated solution fully eliminates human-error vectors. Market data supports sustained evolution, with the global antivirus software sector valued at $4.13 billion in 2024 and forecasted to reach $4.19 billion in 2025 at a of approximately 1.5%, reflecting demand for resilient, consolidated platforms amid rising cyber incidents.

References

Add your contribution
Related Hubs
User Avatar
No comments yet.