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Denial-of-service attack
Denial-of-service attack
from Wikipedia

Diagram of a DDoS attack. Note how multiple computers are attacking a single computer.

In computing, a denial-of-service attack (DoS attack; UK: /dɒs/ doss US: /dɑːs/ daas[1]) is a cyberattack in which the perpetrator seeks to make a machine or network resource unavailable to its intended users by temporarily or indefinitely disrupting services of a host connected to a network. Denial of service is typically accomplished by flooding the targeted machine or resource with superfluous requests in an attempt to overload systems and prevent some or all legitimate requests from being fulfilled.[2] The range of attacks varies widely, spanning from inundating a server with millions of requests to slow its performance, overwhelming a server with a substantial amount of invalid data, to submitting requests with an illegitimate IP address.[3]

In a distributed denial-of-service attack (DDoS attack; UK: /ˈd.dɒs/ DEE-doss US: /ˈd.dɑːs/ DEE-daas[4]), the incoming traffic flooding the victim originates from many different sources. More sophisticated strategies are required to mitigate this type of attack; simply attempting to block a single source is insufficient as there are multiple sources.[5][6] A DDoS attack is analogous to a group of people crowding the entry door of a shop, making it hard for legitimate customers to enter, thus disrupting trade and losing the business money. Criminal perpetrators of DDoS attacks often target sites or services hosted on high-profile web servers such as banks or credit card payment gateways. Revenge and blackmail,[7][8][9] as well as hacktivism,[10] can motivate these attacks.

History

[edit]

Panix, the third-oldest ISP in the world, was the target of what is thought to be the first DoS attack. On September 6, 1996, Panix was subject to a SYN flood attack, which brought down its services for several days while hardware vendors, notably Cisco, figured out a proper defense.[11] The release of sample code during the event led to the online attack of Sprint, EarthLink, E-Trade, and other major corporations in the year to follow.[12]

Cloudflare claims to have recorded and successfully autonomously blocked a 40-second DDoS attack on 23 September, 2025 that reached a peak volume of 22.2 Tb/s, which would be the largest DDoS attack to date.[13] Cloudflare has stated that over 404,000 source IPs were used to target one IP address, and that the source IPs were not spoofed.[14] According to Cloudflare, this came after several other large-scale DDoS attacks, each consecutively beating the previous record, including a 7.3 Tb/s attack in May 2025 and an 11.5 Tb/s attack on 1 September, 2025.[15]

In February 2020, Amazon Web Services experienced an attack with a peak volume of 2.3 Tb/s.[16] In July 2021, Cloudflare boasted of protecting its client from a DDoS attack from a global Mirai botnet that was up to 17.2 million requests per second.[17] Russian DDoS prevention provider Yandex said it blocked a HTTP pipelining DDoS attack on Sept. 5. 2021 that originated from unpatched Mikrotik networking gear.[18] In the first half of 2022, the Russian invasion of Ukraine significantly shaped the cyberthreat landscape, with an increase in cyberattacks attributed to both state-sponsored actors and global hacktivist activities. The most notable event was a DDoS attack in February, the largest Ukraine has encountered, disrupting government and financial sector services. This wave of cyber aggression extended to Western allies like the UK, the US, and Germany. Particularly, the UK's financial sector saw an increase in DDoS attacks from nation-state actors and hacktivists, aimed at undermining Ukraine's allies.[19]

In February 2023, Cloudflare faced a 71 million/requests per second attack which Cloudflare claims was the largest HTTP DDoS attack at the time.[20] HTTP DDoS attacks are measured by HTTP requests per second instead of packets per second or bits per second. On July 10, 2023, the fanfiction platform Archive of Our Own (AO3) faced DDoS attacks, disrupting services. Anonymous Sudan, claiming the attack for religious and political reasons, was viewed skeptically by AO3 and experts. Flashpoint, a threat intelligence vendor, noted the group's past activities but doubted their stated motives. AO3, supported by the non-profit Organization for Transformative Works (OTW) and reliant on donations, is unlikely to meet the $30,000 Bitcoin ransom.[21][22]

In August 2023, the group of hacktivists NoName057 targeted several Italian financial institutions, through the execution of slow DoS attacks.[23] On 14 January 2024, they executed a DDoS attack on Swiss federal websites, prompted by President Zelensky's attendance at the Davos World Economic Forum. Switzerland's National Cyber Security Centre quickly mitigated the attack, ensuring core federal services remained secure, despite temporary accessibility issues on some websites.[24] In October 2023, exploitation of a new vulnerability in the HTTP/2 protocol resulted in the record for largest HTTP DDoS attack being broken twice, once with a 201 million requests per second attack observed by Cloudflare,[25] and again with a 398 million requests per second attack observed by Google.[26] In August 2024, Global Secure Layer observed and reported on a record-breaking packet DDoS at 3.15 billion packets per second, which targeted an undisclosed number of unofficial Minecraft game servers.[27]

In October 2024, the Internet Archive faced two severe DDoS attacks that brought the site completely offline, immediately following a previous attack that leaked records of over 31 million of the site's users.[28][29] The hacktivist group SN_Blackmeta claimed the DDoS attack as retribution for American involvement in the Gaza war, despite the Internet Archive being unaffiliated with the United States government; however, their link with the preceding data leak remains unclear.[30]

Types

[edit]

Denial-of-service attacks are characterized by an explicit attempt by attackers to prevent legitimate use of a service. There are two general forms of DoS attacks: those that crash services and those that flood services. The most serious attacks are distributed.[31]

Distributed DoS

[edit]

A distributed denial-of-service (DDoS; UK: /ˈd.dɒs/ DEE-doss US: /ˈd.dɑːs/ DEE-daas[32]) attack occurs when multiple systems flood the bandwidth or resources of a targeted system, usually one or more web servers.[31] A DDoS attack uses more than one unique IP address or machines, often from thousands of hosts infected with malware.[33][34] A distributed denial of service attack typically involves more than around 3–5 nodes on different networks; fewer nodes may qualify as a DoS attack but is not a DDoS attack.[35][36]

Most of the time, attackers operate from an endpoint that is not their intended target, for example using another user's machine to attack a server. By using another unsuspecting endpoint if it becomes compromised they can then move onto another workstation within the enterprise network.[37] However, even faking lots of users and executing a DoS attack, a single attacker with few computers is still very limited in the amount of traffic they can generate.[38] If the attacks are from multiple sources, it can be difficult for the host to identify and stop them.[39]

The scale of DDoS attacks has continued to rise over recent years, by 2016 exceeding a terabit per second.[40][41] Some common examples of DDoS attacks are UDP flooding, SYN flooding and DNS amplification.[42][43]

Yo-yo attack

[edit]

A yo-yo attack is a specific type of DoS/DDoS aimed at cloud-hosted applications which use autoscaling.[44][45][46] During the attack, an attacker repeatedly changes between sending a lot of traffic (which causes a scale-up) and stopping the burst (causing a scale-down as a result).[47]

Application layer attacks

[edit]

An application layer DDoS attack (sometimes referred to as layer 7 DDoS attack) is a form of DDoS attack where attackers target application-layer processes.[48][35] The attack over-exercises specific functions or features of a website with the intention to disable those functions or features. This application-layer attack is different from an entire network attack, and is often used against financial institutions to distract IT and security personnel from security breaches.[49] In 2013, application-layer DDoS attacks represented 20% of all DDoS attacks.[50] According to research by Akamai Technologies, there have been "51 percent more application layer attacks" from Q4 2013 to Q4 2014 and "16 percent more" from Q3 2014 to Q4 2014.[51] In November 2017; Junade Ali, an engineer at Cloudflare noted that whilst network-level attacks continue to be of high capacity, they were occurring less frequently. Ali further noted that although network-level attacks were becoming less frequent, data from Cloudflare demonstrated that application-layer attacks were still showing no sign of slowing down.[52]

Method of attack

[edit]

The simplest DoS attack relies primarily on brute force, flooding the target with an overwhelming flux of packets, oversaturating its connection bandwidth or depleting the target's system resources. Bandwidth-saturating floods rely on the attacker's ability to generate the overwhelming flux of packets. A common way of achieving this today is via distributed denial-of-service, employing a botnet. An application layer DDoS attack is done mainly for specific targeted purposes, including disrupting transactions and access to databases. It requires fewer resources than network layer attacks but often accompanies them.[53] An attack may be disguised to look like legitimate traffic, except it targets specific application packets or functions. The attack on the application layer can disrupt services such as the retrieval of information or search functions on a website.[50]

Advanced persistent DoS

[edit]

An advanced persistent DoS (APDoS) is associated with an advanced persistent threat and requires specialized DDoS mitigation.[54] These attacks can persist for weeks; the longest continuous period noted so far lasted 38 days. This attack involved approximately 50+ petabits (50,000+ terabits) of malicious traffic.[55] Attackers in this scenario may tactically switch between several targets to create a diversion to evade defensive DDoS countermeasures but all the while eventually concentrating the main thrust of the attack onto a single victim. In this scenario, attackers with continuous access to several very powerful network resources are capable of sustaining a prolonged campaign generating enormous levels of unamplified DDoS traffic. APDoS attacks are characterized by:

  • advanced reconnaissance (pre-attack OSINT and extensive decoyed scanning crafted to evade detection over long periods)
  • tactical execution (attack with both primary and secondary victims but the focus is on primary)
  • explicit motivation (a calculated end game/goal target)
  • large computing capacity (access to substantial computer power and network bandwidth)
  • simultaneous multi-threaded OSI layer attacks (sophisticated tools operating at layers 3 through 7)
  • persistence over extended periods (combining all the above into a concerted, well-managed attack across a range of targets).[56]

Denial-of-service as a service

[edit]

Some vendors provide so-called booter or stresser services, which have simple web-based front ends, and accept payment over the web. Marketed and promoted as stress-testing tools, they can be used to perform unauthorized denial-of-service attacks, and allow technically unsophisticated attackers access to sophisticated attack tools.[57] Usually powered by a botnet, the traffic produced by a consumer stresser can range anywhere from 5-50 Gbit/s, which can, in most cases, deny the average home user internet access.[58]

Markov-modulated denial-of-service attack

[edit]

A Markov-modulated denial-of-service attack occurs when the attacker disrupts control packets using a hidden Markov model. A setting in which Markov-model based attacks are prevalent is online gaming as the disruption of the control packet undermines game play and system functionality.[59]

Symptoms

[edit]

The United States Computer Emergency Readiness Team (US-CERT) has identified symptoms of a denial-of-service attack to include:[60]

  • unusually slow network performance (opening files or accessing websites),
  • unavailability of a particular website, or
  • inability to access any website.

Attack techniques

[edit]

Attack tools

[edit]

In cases such as MyDoom and Slowloris, the tools are embedded in malware and launch their attacks without the knowledge of the system owner. Stacheldraht is a classic example of a DDoS tool. It uses a layered structure where the attacker uses a client program to connect to handlers which are compromised systems that issue commands to the zombie agents which in turn facilitate the DDoS attack. Agents are compromised via the handlers by the attacker using automated routines to exploit vulnerabilities in programs that accept remote connections running on the targeted remote hosts. Each handler can control up to a thousand agents.[61]

In other cases a machine may become part of a DDoS attack with the owner's consent, for example, in Operation Payback organized by the group Anonymous. The Low Orbit Ion Cannon has typically been used in this way. Along with High Orbit Ion Cannon a wide variety of DDoS tools are available today, including paid and free versions, with different features available. There is an underground market for these in hacker-related forums and IRC channels.

Application-layer attacks

[edit]

Application-layer attacks employ DoS-causing exploits and can cause server-running software to fill the disk space or consume all available memory or CPU time. Attacks may use specific packet types or connection requests to saturate finite resources by, for example, occupying the maximum number of open connections or filling the victim's disk space with logs. An attacker with shell-level access to a victim's computer may slow it until it is unusable or crash it by using a fork bomb. Another kind of application-level DoS attack is XDoS (or XML DoS) which can be controlled by modern web application firewalls (WAFs). All attacks belonging to the category of timeout exploiting.[62]

Slow DoS attacks implement an application-layer attack. Examples of threats are Slowloris, establishing pending connections with the victim, or SlowDroid, an attack running on mobile devices. Another target of DDoS attacks may be to produce added costs for the application operator, when the latter uses resources based on cloud computing. In this case, normally application-used resources are tied to a needed quality of service (QoS) level (e.g. responses should be less than 200 ms) and this rule is usually linked to automated software (e.g. Amazon CloudWatch)[63] to raise more virtual resources from the provider to meet the defined QoS levels for the increased requests. The main incentive behind such attacks may be to drive the application owner to raise the elasticity levels to handle the increased application traffic, to cause financial losses, or force them to become less competitive. A banana attack is another particular type of DoS. It involves redirecting outgoing messages from the client back onto the client, preventing outside access, as well as flooding the client with the sent packets. A LAND attack is of this type.

Degradation-of-service attacks

[edit]

Pulsing zombies are compromised computers that are directed to launch intermittent and short-lived floodings of victim websites with the intent of merely slowing it rather than crashing it. This type of attack, referred to as degradation-of-service, can be more difficult to detect and can disrupt and hamper connection to websites for prolonged periods of time, potentially causing more overall disruption than a denial-of-service attack.[64][65] Exposure of degradation-of-service attacks is complicated further by the matter of discerning whether the server is really being attacked or is experiencing higher than normal legitimate traffic loads.[66]

Distributed DoS attack

[edit]

If an attacker mounts an attack from a single host, it would be classified as a DoS attack. Any attack against availability would be classed as a denial-of-service attack. On the other hand, if an attacker uses many systems to simultaneously launch attacks against a remote host, this would be classified as a DDoS attack. Malware can carry DDoS attack mechanisms; one of the better-known examples of this was MyDoom. Its DoS mechanism was triggered on a specific date and time. This type of DDoS involved hardcoding the target IP address before releasing the malware and no further interaction was necessary to launch the attack. A system may also be compromised with a trojan containing a zombie agent. Attackers can also break into systems using automated tools that exploit flaws in programs that listen for connections from remote hosts. This scenario primarily concerns systems acting as servers on the web. Stacheldraht is a classic example of a DDoS tool. It uses a layered structure where the attacker uses a client program to connect to handlers, which are compromised systems that issue commands to the zombie agents, which in turn facilitate the DDoS attack. Agents are compromised via the handlers by the attacker. Each handler can control up to a thousand agents.[61] In some cases a machine may become part of a DDoS attack with the owner's consent, for example, in Operation Payback, organized by the group Anonymous. These attacks can use different types of internet packets such as TCP, UDP, ICMP, etc.

These collections of compromised systems are known as botnets. DDoS tools like Stacheldraht still use classic DoS attack methods centered on IP spoofing and amplification like smurf attacks and fraggle attacks (types of bandwidth consumption attacks). SYN floods (a resource starvation attack) may also be used. Newer tools can use DNS servers for DoS purposes. Unlike MyDoom's DDoS mechanism, botnets can be turned against any IP address. Script kiddies use them to deny the availability of well known websites to legitimate users.[67] More sophisticated attackers use DDoS tools for the purposes of extortion – including against their business rivals.[68] It has been reported that there are new attacks from internet of things (IoT) devices that have been involved in denial of service attacks.[69] In one noted attack that was made peaked at around 20,000 requests per second which came from around 900 CCTV cameras.[70] UK's GCHQ has tools built for DDoS, named PREDATORS FACE and ROLLING THUNDER.[71]

Simple attacks such as SYN floods may appear with a wide range of source IP addresses, giving the appearance of a distributed DoS. These flood attacks do not require completion of the TCP three-way handshake and attempt to exhaust the destination SYN queue or the server bandwidth. Because the source IP addresses can be trivially spoofed, an attack could come from a limited set of sources, or may even originate from a single host. Stack enhancements such as SYN cookies may be effective mitigation against SYN queue flooding but do not address bandwidth exhaustion. In 2022, TCP attacks were the leading method in DDoS incidents, accounting for 63% of all DDoS activity. This includes tactics like TCP SYN, TCP ACK, and TCP floods. With TCP being the most widespread networking protocol, its attacks are expected to remain prevalent in the DDoS threat scene.[19]

DDoS extortion

[edit]

In 2015, DDoS botnets such as DD4BC grew in prominence, taking aim at financial institutions.[72] Cyber-extortionists typically begin with a low-level attack and a warning that a larger attack will be carried out if a ransom is not paid in bitcoin.[73] Security experts recommend targeted websites to not pay the ransom. The attackers tend to get into an extended extortion scheme once they recognize that the target is ready to pay.[74]

HTTP slow POST DoS attack

[edit]

First discovered in 2009, the HTTP slow POST attack sends a complete, legitimate HTTP POST header, which includes a Content-Length field to specify the size of the message body to follow. However, the attacker then proceeds to send the actual message body at an extremely slow rate (e.g. 1 byte/110 seconds). Due to the entire message being correct and complete, the target server will attempt to obey the Content-Length field in the header, and wait for the entire body of the message to be transmitted, which can take a very long time. The attacker establishes hundreds or even thousands of such connections until all resources for incoming connections on the victim server are exhausted, making any further connections impossible until all data has been sent. It is notable that unlike many other DDoS or DDoS attacks, which try to subdue the server by overloading its network or CPU, an HTTP slow POST attack targets the logical resources of the victim, which means the victim would still have enough network bandwidth and processing power to operate.[75] Combined with the fact that the Apache HTTP Server will, by default, accept requests up to 2GB in size, this attack can be particularly powerful. HTTP slow POST attacks are difficult to differentiate from legitimate connections and are therefore able to bypass some protection systems. OWASP, an open source web application security project, released a tool to test the security of servers against this type of attack.[76]

Challenge Collapsar (CC) attack

[edit]

A Challenge Collapsar (CC) attack is an attack where standard HTTP requests are sent to a targeted web server frequently. The Uniform Resource Identifiers (URIs) in the requests require complicated time-consuming algorithms or database operations which may exhaust the resources of the targeted web server.[77][78][79] In 2004, a Chinese hacker nicknamed KiKi invented a hacking tool to send these kinds of requests to attack a NSFOCUS firewall named Collapsar, and thus the hacking tool was known as Challenge Collapsar, or CC for short. Consequently, this type of attack got the name CC attack.[80]

Internet Control Message Protocol (ICMP) flood

[edit]

A smurf attack relies on misconfigured network devices that allow packets to be sent to all computer hosts on a particular network via the broadcast address of the network, rather than a specific machine. The attacker will send large numbers of IP packets with the source address faked to appear to be the address of the victim.[81] Most devices on a network will, by default, respond to this by sending a reply to the source IP address. If the number of machines on the network that receive and respond to these packets is very large, the victim's computer will be flooded with traffic. This overloads the victim's computer and can even make it unusable during such an attack.[82]

Ping flood is based on sending the victim an overwhelming number of ping packets, usually using the ping command from Unix-like hosts.[a] It is very simple to launch, the primary requirement being access to greater bandwidth than the victim. Ping of death is based on sending the victim a malformed ping packet, which will lead to a system crash on a vulnerable system. The BlackNurse attack is an example of an attack taking advantage of the required Destination Port Unreachable ICMP packets.

Nuke

[edit]

A nuke is an old-fashioned denial-of-service attack against computer networks consisting of fragmented or otherwise invalid ICMP packets sent to the target, achieved by using a modified ping utility to repeatedly send this corrupt data, thus slowing down the affected computer until it comes to a complete stop. A specific example of a nuke attack that gained some prominence is the WinNuke, which exploited the vulnerability in the NetBIOS handler in Windows 95. A string of out-of-band data was sent to TCP port 139 of the victim's machine, causing it to lock up and display a Blue Screen of Death.[83]

Peer-to-peer attacks

[edit]

Attackers have found a way to exploit a number of bugs in peer-to-peer servers to initiate DDoS attacks. The most aggressive of these peer-to-peer-DDoS attacks exploits DC++.[citation needed][ambiguous] With peer-to-peer there is no botnet and the attacker does not have to communicate with the clients it subverts. Instead, the attacker acts as a puppet master, instructing clients of large peer-to-peer file sharing hubs to disconnect from their peer-to-peer network and to connect to the victim's website instead.[84][85][86]

Permanent denial-of-service attacks

[edit]

Permanent denial-of-service (PDoS), also known loosely as phlashing,[87] is an attack that damages a system so badly that it requires replacement or reinstallation of hardware.[88] Unlike the distributed denial-of-service attack, a PDoS attack exploits security flaws which allow remote administration on the management interfaces of the victim's hardware, such as routers, printers, or other networking hardware. The attacker uses these vulnerabilities to replace a device's firmware with a modified, corrupt, or defective firmware image—a process which when done legitimately is known as flashing. The intent is to brick the device, rendering it unusable for its original purpose until it can be repaired or replaced. The PDoS is a pure hardware-targeted attack that can be much faster and requires fewer resources than using a botnet in a DDoS attack. Because of these features, and the potential and high probability of security exploits on network-enabled embedded devices, this technique has come to the attention of numerous hacking communities. BrickerBot, a piece of malware that targeted IoT devices, used PDoS attacks to disable its targets.[89] PhlashDance is a tool created by Rich Smith (an employee of Hewlett-Packard's Systems Security Lab) used to detect and demonstrate PDoS vulnerabilities at the 2008 EUSecWest Applied Security Conference in London, UK.[90]

Reflected attack

[edit]

A distributed denial-of-service attack may involve sending forged requests of some type to a very large number of computers that will reply to the requests. Using Internet Protocol address spoofing, the source address is set to that of the targeted victim, which means all the replies will go to (and flood) the target. This reflected attack form is sometimes called a distributed reflective denial-of-service (DRDoS) attack.[91] ICMP echo request attacks (Smurf attacks) can be considered one form of reflected attack, as the flooding hosts send Echo Requests to the broadcast addresses of mis-configured networks, thereby enticing hosts to send Echo Reply packets to the victim. Some early DDoS programs implemented a distributed form of this attack.

Amplification

[edit]

Amplification attacks are used to magnify the bandwidth that is sent to a victim. Many services can be exploited to act as reflectors, some harder to block than others.[92] US-CERT have observed that different services may result in different amplification factors, as tabulated below:[93]

UDP-based amplification attacks
Protocol Amplification factor Notes
Mitel MiCollab 2,200,000,000[94]
Memcached 50,000 Fixed in version 1.5.6[95]
NTP 556.9 Fixed in version 4.2.7p26[96]
CHARGEN 358.8
DNS up to 179[97]
QOTD 140.3
Quake Network Protocol 63.9 Fixed in version 71
BitTorrent 4.0 - 54.3[98] Fixed in libuTP since 2015
CoAP 10 - 50
ARMS 33.5
SSDP 30.8
Kad 16.3
SNMPv2 6.3
Steam Protocol 5.5
NetBIOS 3.8

DNS amplification attacks involves an attacker sending a DNS name lookup request to one or more public DNS servers, spoofing the source IP address of the targeted victim. The attacker tries to request as much information as possible, thus amplifying the DNS response that is sent to the targeted victim. Since the size of the request is significantly smaller than the response, the attacker is easily able to increase the amount of traffic directed at the target.[99][100]

Simple Network Management Protocol (SNMP) and Network Time Protocol (NTP) can also be exploited as reflectors in an amplification attack. An example of an amplified DDoS attack through the NTP is through a command called monlist, which sends the details of the last 600 hosts that have requested the time from the NTP server back to the requester. A small request to this time server can be sent using a spoofed source IP address of some victim, which results in a response 556.9 times the size of the request being sent to the victim. This becomes amplified when using botnets that all send requests with the same spoofed IP source, which will result in a massive amount of data being sent back to the victim. It is very difficult to defend against these types of attacks because the response data is coming from legitimate servers. These attack requests are also sent through UDP, which does not require a connection to the server. This means that the source IP is not verified when a request is received by the server. To bring awareness of these vulnerabilities, campaigns have been started that are dedicated to finding amplification vectors which have led to people fixing their resolvers or having the resolvers shut down completely.[citation needed]

Mirai botnet

[edit]

The Mirai botnet works by using a computer worm to infect hundreds of thousands of IoT devices across the internet. The worm propagates through networks and systems taking control of poorly protected IoT devices such as thermostats, Wi-Fi-enabled clocks, and washing machines.[101] The owner or user will usually have no immediate indication of when the device becomes infected. The IoT device itself is not the direct target of the attack, it is used as a part of a larger attack.[102] Once the hacker has enslaved the desired number of devices, they instruct the devices to try to contact an ISP. In October 2016, a Mirai botnet attacked Dyn which is the ISP for sites such as Twitter, Netflix, etc.[101] As soon as this occurred, these websites were all unreachable for several hours.

R-U-Dead-Yet? (RUDY)

[edit]

RUDY attack targets web applications by starvation of available sessions on the web server. Much like Slowloris, RUDY keeps sessions at halt using never-ending POST transmissions and sending an arbitrarily large content-length header value.[103]

SACK Panic

[edit]

Manipulating maximum segment size and selective acknowledgement (SACK) may be used by a remote peer to cause a denial of service by an integer overflow in the Linux kernel, potentially causing a kernel panic.[104] Jonathan Looney discovered CVE-2019-11477, CVE-2019-11478, CVE-2019-11479 on June 17, 2019.[105]

Shrew attack

[edit]

The shrew attack is a denial-of-service attack on the Transmission Control Protocol where the attacker employs man-in-the-middle techniques. It exploits a weakness in TCP's re-transmission timeout mechanism, using short synchronized bursts of traffic to disrupt TCP connections on the same link.[106]

Slow read attack

[edit]

A slow read attack sends legitimate application layer requests, but reads responses very slowly, keeping connections open longer hoping to exhaust the server's connection pool. The slow read is achieved by advertising a very small number for the TCP Receive Window size, and at the same time emptying clients' TCP receive buffer slowly, which causes a very low data flow rate.[107]

Sophisticated low-bandwidth Distributed Denial-of-Service Attack

[edit]

A sophisticated low-bandwidth DDoS attack is a form of DoS that uses less traffic and increases its effectiveness by aiming at a weak point in the victim's system design, i.e., the attacker sends traffic consisting of complicated requests to the system.[108] Essentially, a sophisticated DDoS attack is lower in cost due to its use of less traffic, is smaller in size making it more difficult to identify, and it has the ability to hurt systems which are protected by flow control mechanisms.[108][109]

SYN flood

[edit]

A SYN flood occurs when a host sends a flood of TCP/SYN packets, often with a forged sender address. Each of these packets is handled like a connection request, causing the server to spawn a half-open connection, send back a TCP/SYN-ACK packet, and wait for a packet in response from the sender address. However, because the sender's address is forged, the response never comes. These half-open connections exhaust the available connections the server can make, keeping it from responding to legitimate requests until after the attack ends.[110]

Teardrop attacks

[edit]

A teardrop attack involves sending mangled IP fragments with overlapping, oversized payloads to the target machine. This can crash various operating systems because of a bug in their TCP/IP fragmentation re-assembly code.[111] Windows 3.1x, Windows 95 and Windows NT operating systems, as well as versions of Linux prior to versions 2.0.32 and 2.1.63 are vulnerable to this attack.[b] One of the fields in an IP header is the fragment offset field, indicating the starting position, or offset, of the data contained in a fragmented packet relative to the data in the original packet. If the sum of the offset and size of one fragmented packet differs from that of the next fragmented packet, the packets overlap. When this happens, a server vulnerable to teardrop attacks is unable to reassemble the packets resulting in a denial-of-service condition.[114]

Telephony denial-of-service

[edit]

Voice over IP has made abusive origination of large numbers of telephone voice calls inexpensive and easily automated while permitting call origins to be misrepresented through caller ID spoofing. According to the US Federal Bureau of Investigation, telephony denial-of-service (TDoS) has appeared as part of various fraudulent schemes:

  • A scammer contacts the victim's banker or broker, impersonating the victim to request a funds transfer. The banker's attempt to contact the victim for verification of the transfer fails as the victim's telephone lines are being flooded with bogus calls, rendering the victim unreachable.[115]
  • A scammer contacts consumers with a bogus claim to collect an outstanding payday loan for thousands of dollars. When the consumer objects, the scammer retaliates by flooding the victim's employer with automated calls. In some cases, the displayed caller ID is spoofed to impersonate police or law enforcement agencies.[116]
  • Swatting: A scammer contacts consumers with a bogus debt collection demand and threatens to send police; when the victim balks, the scammer floods local police numbers with calls on which caller ID is spoofed to display the victim's number. Police soon arrive at the victim's residence attempting to find the origin of the calls.

TDoS can exist even without Internet telephony. In the 2002 New Hampshire Senate election phone jamming scandal, telemarketers were used to flood political opponents with spurious calls to jam phone banks on election day. Widespread publication of a number can also flood it with enough calls to render it unusable, as happened by accident in 1981 with multiple +1-area code-867-5309 subscribers inundated by hundreds of calls daily in response to the song "867-5309/Jenny". TDoS differs from other telephone harassment (such as prank calls and obscene phone calls) by the number of calls originated. By occupying lines continuously with repeated automated calls, the victim is prevented from making or receiving both routine and emergency telephone calls. Related exploits include SMS flooding attacks and black fax or continuous fax transmission by using a loop of paper at the sender.

TTL expiry attack

[edit]

It takes more router resources to drop a packet with a TTL value of 1 or less than it does to forward a packet with a higher TTL value. When a packet is dropped due to TTL expiry, the router CPU must generate and send an ICMP time exceeded response. Generating many of these responses can overload the router's CPU.[117]

UPnP attack

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A UPnP attack uses an existing vulnerability in Universal Plug and Play (UPnP) protocol to get past network security and flood a target's network and servers. The attack is based on a DNS amplification technique, but the attack mechanism is a UPnP router that forwards requests from one outer source to another. The UPnP router returns the data on an unexpected UDP port from a bogus IP address, making it harder to take simple action to shut down the traffic flood. According to the Imperva researchers, the most effective way to stop this attack is for companies to lock down UPnP routers.[118][119]

SSDP reflection attack

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In 2014, it was discovered that Simple Service Discovery Protocol (SSDP) was being used in DDoS attacks known as an SSDP reflection attack with amplification. Many devices, including some residential routers, have a vulnerability in the UPnP software that allows an attacker to get replies from UDP port 1900 to a destination address of their choice. With a botnet of thousands of devices, the attackers can generate sufficient packet rates and occupy bandwidth to saturate links, causing the denial of services.[120][121][122] Because of this weakness, the network company Cloudflare has described SSDP as the "Stupidly Simple DDoS Protocol".[122]

ARP spoofing

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ARP spoofing is a common DoS attack that involves a vulnerability in the ARP protocol that allows an attacker to associate their MAC address to the IP address of another computer or gateway, causing traffic intended for the original authentic IP to be re-routed to that of the attacker, causing a denial of service.

Defense techniques

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Defensive responses to denial-of-service attacks typically involve the use of a combination of attack detection, traffic classification and response tools, aiming to block traffic the tools identify as illegitimate and allow traffic that they identify as legitimate.[123] A list of response tools include the following.

Upstream filtering

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All traffic destined to the victim is diverted to pass through a cleaning center or a scrubbing center via various methods such as: changing the victim IP address in the DNS system, tunneling methods (GRE/VRF, MPLS, SDN),[124] proxies, digital cross connects, or even direct circuits. The cleaning center separates bad traffic (DDoS and also other common internet attacks) and only passes good legitimate traffic to the victim server.[125] The victim needs central connectivity to the Internet to use this kind of service unless they happen to be located within the same facility as the cleaning center. DDoS attacks can overwhelm any type of hardware firewall, and passing malicious traffic through large and mature networks becomes more and more effective and economically sustainable against DDoS.[126]

Application front end hardware

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Application front-end hardware is intelligent hardware placed on the network before traffic reaches the servers. It can be used on networks in conjunction with routers and switches and as part of bandwidth management. Application front-end hardware analyzes data packets as they enter the network, and identifies and drops dangerous or suspicious flows.

Application level key completion indicators

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Approaches to detection of DDoS attacks against cloud-based applications may be based on an application layer analysis, indicating whether incoming bulk traffic is legitimate.[127] These approaches mainly rely on an identified path of value inside the application and monitor the progress of requests on this path, through markers called key completion indicators.[128] In essence, these techniques are statistical methods of assessing the behavior of incoming requests to detect if something unusual or abnormal is going on. An analogy is to a brick-and-mortar department store where customers spend, on average, a known percentage of their time on different activities such as picking up items and examining them, putting them back, filling a basket, waiting to pay, paying, and leaving. If a mob of customers arrived in the store and spent all their time picking up items and putting them back, but never made any purchases, this could be flagged as unusual behavior.

Blackholing and sinkholing

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With blackhole routing, all the traffic to the attacked DNS or IP address is sent to a black hole (null interface or a non-existent server). To be more efficient and avoid affecting network connectivity, it can be managed by the ISP.[129] A DNS sinkhole routes traffic to a valid IP address which analyzes traffic and rejects bad packets. Sinkholing may not be efficient for severe attacks.

IPS based prevention

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Intrusion prevention systems (IPS) are effective if the attacks have signatures associated with them. However, the trend among attacks is to have legitimate content but bad intent. Intrusion-prevention systems that work on content recognition cannot block behavior-based DoS attacks.[54] An ASIC based IPS may detect and block denial-of-service attacks because they have the processing power and the granularity to analyze the attacks and act like a circuit breaker in an automated way.[54]

DDS based defense

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More focused on the problem than IPS, a DoS defense system (DDS) can block connection-based DoS attacks and those with legitimate content but bad intent. A DDS can also address both protocol attacks (such as teardrop and ping of death) and rate-based attacks (such as ICMP floods and SYN floods). DDS has a purpose-built system that can easily identify and obstruct denial of service attacks at a greater speed than a software-based system.[130]

Firewalls

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In the case of a simple attack, a firewall can be adjusted to deny all incoming traffic from the attackers, based on protocols, ports, or the originating IP addresses. More complex attacks will however be hard to block with simple rules: for example, if there is an ongoing attack on port 80 (web service), it is not possible to drop all incoming traffic on this port because doing so will prevent the server from receiving and serving legitimate traffic.[131] Additionally, firewalls may be too deep in the network hierarchy, with routers being adversely affected before the traffic gets to the firewall. Also, many security tools still do not support IPv6 or may not be configured properly, so the firewalls may be bypassed during the attacks.[132]

Routers

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Similar to switches, routers have some rate-limiting and ACL capabilities. They, too, are manually set. Most routers can be easily overwhelmed under a DoS attack. Nokia SR-OS using FP4 or FP5 processors offers DDoS protection.[133] Nokia SR-OS also uses big data analytics-based Nokia Deepfield Defender for DDoS protection.[134] Cisco IOS has optional features that can reduce the impact of flooding.[135]

Switches

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Most switches have some rate-limiting and ACL capability. Some switches provide automatic or system-wide rate limiting, traffic shaping, delayed binding (TCP splicing), deep packet inspection and bogon filtering (bogus IP filtering) to detect and remediate DoS attacks through automatic rate filtering and WAN Link failover and balancing. These schemes will work as long as the DoS attacks can be prevented by using them. For example, SYN flood can be prevented using delayed binding or TCP splicing. Similarly, content-based DoS may be prevented using deep packet inspection. Attacks using Martian packets can be prevented using bogon filtering. Automatic rate filtering can work as long as set rate thresholds have been set correctly. WAN-link failover will work as long as both links have a DoS prevention mechanism.[54]

Blocking vulnerable ports

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Threats may be associated with specific TCP or UDP port numbers. Blocking these ports at the firewall can mitigate an attack. For example, in an SSDP reflection attack, the key mitigation is to block incoming UDP traffic on port 1900.[136]

Blocking based on TTL

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Blocking specific Time to live (TTL) values based on the network path length can be a viable option for blocking spoofed attacks.[137]

Unintentional denial-of-service

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An unintentional denial-of-service can occur when a system ends up denied, not due to a deliberate attack by a single individual or group of individuals, but simply due to a sudden enormous spike in popularity. This can happen when an extremely popular website posts a prominent link to a second, less well-prepared site, for example, as part of a news story. The result is that a significant proportion of the primary site's regular users – potentially hundreds of thousands of people – click that link in the space of a few hours, having the same effect on the target website as a DDoS attack. A VIPDoS is the same, but specifically when the link was posted by a celebrity. When Michael Jackson died in 2009, websites such as Google and Twitter slowed down or even crashed.[138] Many sites' servers thought the requests were from a virus or spyware trying to cause a denial-of-service attack, warning users that their queries looked like "automated requests from a computer virus or spyware application".[139]

News sites and link sites – sites whose primary function is to provide links to interesting content elsewhere on the Internet – are most likely to cause this phenomenon. The canonical example is the Slashdot effect when receiving traffic from Slashdot. It is also known as "the Reddit hug of death"[140] and "the Digg effect".[141]

Similar unintentional denial-of-service can also occur via other media, e.g. when a URL is mentioned on television. In March 2014, after Malaysia Airlines Flight 370 went missing, DigitalGlobe launched a crowdsourcing service on which users could help search for the missing jet in satellite images. The response overwhelmed the company's servers.[142] An unintentional denial-of-service may also result from a prescheduled event created by the website itself, as was the case of the Census in Australia in 2016.[143]

Legal action has been taken in at least one such case. In 2006, Universal Tube & Rollform Equipment Corporation sued YouTube: massive numbers of would-be YouTube.com users accidentally typed the tube company's URL, utube.com. As a result, the tube company ended up having to spend large amounts of money on upgrading its bandwidth.[144]

Routers have also been known to create unintentional DoS attacks, as both D-Link and Netgear routers have overloaded NTP servers by flooding them without respecting the restrictions of client types or geographical limitations.

Side effects of attacks

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Backscatter

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In computer network security, backscatter is a side-effect of a spoofed denial-of-service attack. In this kind of attack, the attacker spoofs the source address in IP packets sent to the victim. In general, the victim machine cannot distinguish between the spoofed packets and legitimate packets, so the victim responds to the spoofed packets as it normally would. These response packets are known as backscatter.[145]

If the attacker is spoofing source addresses randomly, the backscatter response packets from the victim will be sent back to random destinations. This effect can be used by network telescopes as indirect evidence of such attacks. The term backscatter analysis refers to observing backscatter packets arriving at a statistically significant portion of the IP address space to determine the characteristics of DoS attacks and victims.

Legality

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Numerous websites offering tools to conduct a DDoS attack were seized by the FBI under the Computer Fraud and Abuse Act.[146]

Many jurisdictions have laws under which denial-of-service attacks are illegal. UNCTAD highlights that 156 countries, or 80% globally, have enacted cybercrime laws to combat its widespread impact. Adoption rates vary by region, with Europe at a 91% rate, and Africa at 72%.[147]

In the US, denial-of-service attacks may be considered a federal crime under the Computer Fraud and Abuse Act with penalties that include years of imprisonment.[148] The Computer Crime and Intellectual Property Section of the US Department of Justice handles cases of DoS and DDoS. In one example, in July 2019, Austin Thompson, aka DerpTrolling, was sentenced to 27 months in prison and $95,000 restitution by a federal court for conducting multiple DDoS attacks on major video gaming companies, disrupting their systems from hours to days.[149][150]

In European countries, committing criminal denial-of-service attacks may, as a minimum, lead to arrest.[151] The United Kingdom is unusual in that it specifically outlawed denial-of-service attacks and set a maximum penalty of 10 years in prison with the Police and Justice Act 2006, which amended Section 3 of the Computer Misuse Act 1990.[152]

In January 2019, Europol announced that "actions are currently underway worldwide to track down the users" of Webstresser.org, a former DDoS marketplace that was shut down in April 2018 as part of Operation PowerOFF.[153] Europol said UK police were conducting a number of "live operations" targeting over 250 users of Webstresser and other DDoS services.[154]

On January 7, 2013, Anonymous posted a petition on the whitehouse.gov site asking that DDoS be recognized as a legal form of protest similar to the Occupy movement, the claim being that the similarity in the purpose of both is same.[155]

See also

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Notes

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References

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Further reading

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
A denial-of-service (DoS) attack is a cyber operation in which an attacker disrupts the of a targeted computer system, network, or service to its authorized users by overwhelming it with excessive resource demands or exploiting protocol weaknesses. These attacks typically involve flooding the target with illegitimate traffic to exhaust bandwidth, processing power, or memory, thereby preventing legitimate access. Distributed denial-of-service (DDoS) variants amplify this effect by coordinating assaults from multiple compromised devices, such as botnets, making mitigation more challenging due to the scale and dispersion of sources. Common mechanisms include volumetric floods that saturate network links, protocol exploits like floods that tie up connection tables, and application-layer attacks that mimic legitimate requests to drain server resources. DoS attacks have evolved since the , transitioning from single-source disruptions to sophisticated DDoS campaigns leveraging amplification techniques and rented services. Such attacks inflict significant operational harms, including temporary outages of , financial losses from downtime and recovery efforts, and erosion of user trust in affected services. In sectors like and healthcare, DDoS incidents can cascade to broader economic disruptions or endanger public safety by impeding emergency responses. Perpetrators often pursue motives ranging from via ransomware-like demands, geopolitical , or ideological disruption by hacktivists targeting perceived adversaries. DoS and DDoS actions constitute federal crimes in the United States under the , punishable by fines and imprisonment up to ten years or more depending on intent and damage caused. International enforcement collaborates through agencies like the FBI to dismantle botnets and "stresser" services that democratize attack capabilities. Despite defensive advancements like traffic scrubbing and , the low barrier to entry—via commoditized tools—and attribution difficulties sustain their prevalence as a persistent threat vector.

Fundamentals

Definition and Mechanism

A denial-of-service (DoS) attack constitutes a cyber assault wherein an attacker renders a target machine, network, or service unavailable to its legitimate users by overwhelming it with excessive traffic or exploiting vulnerabilities to exhaust computational resources. This disruption arises from the target's inability to handle the influx, leading to degraded performance or complete downtime, as finite resources like bandwidth, CPU cycles, and become saturated. The core mechanism relies on resource exhaustion through flooding techniques, where the attacker generates a high volume of illegitimate requests or packets directed at the victim. In volumetric DoS attacks, massive data payloads consume network bandwidth, preventing legitimate packets from reaching the target; for example, UDP floods send spoofed packets to random ports, prompting error responses that amplify traffic back to the victim. Protocol-based attacks, such as SYN floods, exploit TCP handshake processes by sending numerous SYN packets with spoofed IP addresses, forcing the server to allocate resources for incomplete connections until its connection table overflows. Application-layer mechanisms mimic valid user behavior, like HTTP GET floods, to tie up server processing by handling each request individually, often evading basic filters. When executed from a single source, the attack is termed a classic DoS; however, leveraging multiple compromised devices—such as botnets—escalates it to a distributed denial-of-service (DDoS), multiplying the traffic volume and complicating mitigation due to the dispersed origins. Success depends on the attacker's ability to outpace the target's capacity, with effects ranging from temporary slowdowns to prolonged outages measured in hours or days. A denial-of-service (DoS) attack differs from a distributed denial-of-service (DDoS) attack primarily in the scale and origin of the disruptive traffic: DoS originates from a single system or network source attempting to overwhelm the target, whereas DDoS leverages multiple compromised systems, often forming a botnet, to generate traffic from diverse locations, making detection and mitigation more challenging. This multiplicity in DDoS amplifies volume and complicates traceback, as the distributed nature evades simple IP blocking that might suffice against a solitary DoS perpetrator. Unlike malware infections, which involve deploying malicious code to infiltrate systems, exfiltrate data, or alter functionality—targeting or under the CIA triad—DoS attacks focus exclusively on disrupting without breaching or modifying the target's data. may incidentally cause denial of service through resource exhaustion, but its core intent is persistence and execution, such as or backdoor installation, rather than transient overload. DoS attacks also contrast with ransomware, a subset of malware that encrypts victim files or locks systems to extort payment, thereby compromising data integrity and access restoration hinges on decryption keys rather than traffic normalization. While some ransomware campaigns incorporate DoS elements for added pressure, the primary mechanism remains encryption-based extortion, not pure volumetric flooding or protocol exploitation inherent to DoS. In distinction from phishing or social engineering attacks, which exploit human vulnerabilities to elicit credentials or actions leading to unauthorized access, DoS relies on technical overload without user interaction, aiming to render services inoperable for all users indiscriminately rather than targeting specific data disclosure. Similarly, unlike exploits such as or buffer overflows that seek or code execution for deeper compromise, DoS eschews vulnerability probing in favor of resource saturation, providing no direct pathway to system control. These differences underscore DoS as a availability-centric , often serving as a smokescreen for concurrent intrusions but not inherently enabling them.

Historical Development

Origins in Early Networking

The earliest recorded intentional denial-of-service (DoS) attack occurred in 1974, when 13-year-old David Dennis, a student at University High School in , developed a program that simultaneously accessed all 31 terminals connected to the PLATO educational computer system hosted at the University of . This action overwhelmed the system's limited processing capacity, rendering it unresponsive and effectively denying service to legitimate users for an extended period. PLATO, operational since 1960 and networked across multiple institutions by the early 1970s, represented one of the first wide-scale computer networks, making this incident a precursor to modern DoS tactics rooted in resource exhaustion. In the ARPANET era of the late 1970s and early 1980s, network vulnerabilities stemmed from inherent design limitations, such as shared resources and rudimentary traffic controls, which amplified the impact of even unintentional floods. For instance, in 1974, email inventor accidentally inundated the with excessive messages during testing, causing temporary overloads, though this was not malicious. Intentional exploits remained rare due to the network's small scale—limited to about 200 hosts by 1983—and reliance on trusted academic and military users, but early experiments demonstrated how synchronized requests could propagate failures across interconnected nodes. By the 1980s, as transitioned toward TCP/IP protocols formalized in 1983, DoS-like effects emerged from self-propagating code rather than direct flooding. The 1988 , authored by , infected approximately 10% of hosts by exploiting buffer overflows and weak , consuming CPU and memory resources to the point of system denial for thousands of users. While primarily a worm for propagation rather than targeted disruption, its unintended resource hogging highlighted causal vulnerabilities in early , such as unpatched software and lack of , paving the way for deliberate DoS strategies. These incidents underscored that DoS in nascent networks arose from exploiting finite computational and bandwidth constraints, often without sophisticated tools.

Key Milestones from 1990s to 2000s

In September 1996, New York-based Panix endured the first widely publicized denial-of-service attack, where an attacker sent spoofed TCP packets at rates of 150 to 210 per second, exhausting server resources and halting services for approximately 36 hours over multiple days. This single-source assault underscored the fragility of early infrastructure to protocol manipulation, prompting hardware vendors and network administrators to investigate defenses against incomplete exploits. The transition to distributed denial-of-service (DDoS) capabilities accelerated in 1999 with the appearance of automated tools leveraging compromised hosts as bots. Trinoo, the earliest documented DDoS program, surfaced around June or July 1999 and was deployed in August against the , coordinating up to 200 agents to generate UDP floods exceeding 100 Mbps, marking the first known multi-source attack of significant scale. Shortly after, Stacheldraht emerged as an advanced variant, incorporating encrypted command-and-control channels, automated updates, and support for SYN floods, ICMP floods, and Smurf attacks to enhance stealth and versatility across Unix systems. February 2000 epitomized the maturing threat when 15-year-old , alias MafiaBoy, exploited botnets built with Trinoo and Tribe Flood Network to launch DDoS campaigns against high-profile targets including Yahoo (outage of over 20 hours), , , Amazon, and , generating traffic volumes that crippled operations and reportedly inflicted global economic losses nearing $1.2 billion. These coordinated assaults, peaking at 1 Gbps in some instances, exposed the commercial sector's underpreparedness and catalyzed regulatory responses, including FBI investigations and congressional hearings on cyber vulnerabilities. By the mid-2000s, DDoS incidents proliferated, with state-linked attacks like the 2007 barrage on Estonian government and financial sites—saturating networks with up to 90 Mbps of UDP and HTTP floods—illustrating geopolitical weaponization, though attribution remains contested amid evidence of Russian IP origins and volunteer coordination.

Evolution in the 2010s and Beyond

In the , denial-of-service attacks evolved toward greater accessibility and scale through the rise of commercial DDoS-for-hire services, often termed booters or stressers, which enabled users lacking advanced skills to launch assaults via web interfaces for nominal fees starting around $10 per attack. These platforms rented access to pre-compromised botnets, proliferating after approximately and contributing to a surge in opportunistic incidents, with noting hundreds of such services by mid-decade. Concurrently, attackers refined reflection and amplification methods, exploiting open UDP-based protocols like DNS and NTP to multiply traffic; this shift allowed smaller botnets to generate outsized volumes, as evidenced by the March 2013 assault on Spamhaus, which peaked at 300 Gbps using DNS reflection and disrupted European internet peering points. The mid-2010s introduction of large-scale IoT botnets represented a pivotal advancement, capitalizing on the rapid deployment of poorly secured connected devices such as cameras and routers. The Mirai malware, emerging in August 2016, scanned for vulnerable IoT endpoints using default credentials, amassing over 600,000 bots and enabling terabit-per-second attacks; its October 21, 2016, barrage against DNS provider Dyn exceeded 1.2 Tbps via volumetric floods, causing multi-hour outages for East Coast users accessing sites like Twitter, Netflix, and Reddit. The subsequent leak of Mirai's source code in January 2017 fueled variants like Satori and Okiru, sustaining IoT-driven DDoS into the 2020s and demonstrating how device proliferation—reaching billions of endpoints—amplified attack potential without proportional security improvements. Late-decade innovations in amplification pushed boundaries further, with attackers abusing Memcached servers offering gains up to 51,000-fold; the February 28, 2018, attack on GitHub reached 1.3 Tbps using this vector, sustained for minutes before mitigation via upstream scrubbing. Into the 2020s, volumetric peaks escalated amid cloud resource exploitation, including misconfigured virtual machines and containers, as in the 3.47 Tbps assault on Microsoft Azure in November 2021, which combined multiple vectors at 340 million packets per second. Recent developments, such as the 2025 ShadowV2 botnet leveraging AWS Docker instances for DDoS-as-a-service, underscore a trend toward cloud-native threats that evade traditional defenses by mimicking legitimate traffic. Overall, attacks diversified into multi-vector campaigns blending volumetric floods with protocol and application-layer exploits, complicating detection as vectors fragmented beyond the dominant types of 2010, which accounted for 90% of incidents.

Motivations and Actors

Criminal and Financial Motives

![FBI seizure notice for DDoS domain][float-right]
Criminals frequently employ denial-of-service (DoS) attacks to extort payments from targets, demanding ransoms to halt or prevent disruptions to online services. These ransom DoS (RDoS) campaigns often target financial institutions, retailers, and operations, where translates to significant losses, making victims more likely to comply. For instance, in 2020, an extortion campaign specifically aimed at financial firms and retailers involved threats of DoS attacks unless payments were made, exploiting the high stakes of uninterrupted digital operations.
DoS-for-hire services, known as booters or stressers, enable less technically skilled criminals to launch attacks for profit, often renting botnets or amplification tools for fees ranging from minimal amounts to thousands of dollars per . These platforms have facilitated widespread criminal activity, with one prominent service linked to tens of thousands of weekly attacks before infiltration by authorities in 2024. Operators of such services profit by charging subscribers for access, while users deploy attacks for competitive , such as overwhelming rival sites during peak sales to divert customers and revenue. actions, including the shutdown of 27 DoS booter operations in December 2024 by Europol-coordinated efforts across 15 countries, underscore the organized criminal networks behind these financial incentives. In specific cases, attackers have targeted high-value sectors like with massive threats, such as an 800 Gbps DoS attempt aimed at . A Latin American banking conglomerate faced a direct email in an undisclosed year, threatening DoS unless demands were met, highlighting vulnerabilities in . Broader campaigns have struck over 100 financial firms with similar threats, methodically disrupting websites to coerce payments and demonstrating the scalability of DoS for monetary gain. The economic pressure on victims—estimated at up to $40,000 per hour of —amplifies the effectiveness of these motives, as businesses weigh costs against prolonged outages.

Hacktivist and Ideological Campaigns

Hacktivists, motivated by political, social, or ideological grievances, deploy denial-of-service attacks to disrupt digital infrastructure associated with perceived adversaries, thereby amplifying dissent and imposing operational costs. These campaigns differ from criminal extortion by prioritizing symbolic disruption over financial gain, often targeting government portals, media platforms, or corporate sites to protest policies on , , or international conflicts. Coordinated via online forums, such efforts leverage accessible botnets or volunteer-driven tools to flood targets, reflecting a form of digital activism that emerged prominently in the . The Anonymous collective has conducted numerous DDoS operations framed as ideological resistance, utilizing tools like the (LOIC) for HTTP floods and UDP attacks against entities accused of suppressing information or enforcing unpopular regulations. In recent years, self-proclaimed hacktivist group Anonymous Sudan executed large-scale DDoS campaigns in 2023 and 2024, targeting Western organizations including hospitals, services, and OpenAI's , with stated motives tied to geopolitical issues such as opposition to or solidarity with . U.S. authorities indicted two Sudanese nationals for controlling the group, charging them with conspiracy to damage protected computers, underscoring how such actions often cross into prosecutable despite hacktivist rhetoric. Geopolitical tensions have spurred ideological DDoS by loosely affiliated nationalists or activists, as seen in the August 2023 attacks by Russian hacktivists on Czech banks and the , which severed access in retaliation for Czech support of . Similarly, amid 2025 India-Pakistan escalations, opposing hacktivist factions unleashed Web DDoS, botnets, and defacements on to assert territorial or ideological claims. In the Iran-Israel cyber confrontations, groups like "Mr Hamza" and "Arabian Ghosts" primarily relied on DDoS against government and military-linked sites, illustrating the tactic's role in low-cost, high-visibility proxy conflicts. While these attacks generate media attention, their transient effects frequently fail to alter policy, instead prompting enhanced defenses and international condemnations.

State-Sponsored and Geopolitical Uses

State actors have employed denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks as tools of geopolitical coercion, aiming to disrupt , signal resolve, and impose economic costs without resorting to kinetic military action. These operations often serve as asymmetric responses to perceived provocations, such as diplomatic disputes or sanctions, exploiting the low for DDoS while complicating attribution due to the use of botnets and proxies. Unlike criminal or hacktivist motives, state-sponsored variants prioritize strategic disruption over financial gain, targeting , financial, and media sectors to erode public confidence and operational capacity. In April-May 2007, faced coordinated DDoS attacks following the relocation of a Soviet-era from , paralyzing government websites, banks, and media outlets for days and causing widespread service outages. The attacks involved volumetric floods from compromised machines, many traced to IP addresses in , with command-and-control servers hosted on Russian infrastructure; Estonian authorities and analysts identified indicators of orchestration by Russian state-linked actors, including pro-Kremlin youth groups like Nashi, amid heightened bilateral tensions. While definitive attribution remains challenging due to the distributed nature of , the political timing and traffic patterns originating from Russian-language sources pointed to state encouragement or facilitation rather than independent criminal activity. North Korea has repeatedly deployed DDoS capabilities against South Korean and U.S. targets as part of its broader cyber strategy to counter military exercises and sanctions. In July 2009, attacks overwhelmed South Korean government and banking sites with SYN floods and other volumetric methods, disrupting services for hours; U.S. State Department and South Korean intelligence attributed these to North Korea's , citing code similarities with prior state operations. Subsequent waves in 2011 targeted financial institutions and media under the "HIDDEN COBRA" campaign, using custom to sustain botnet-driven floods, reflecting Pyongyang's use of cyber tools for and retaliation against perceived aggression. Iran-linked actors conducted from September 2012 to early 2013, launching sustained DDoS campaigns against major U.S. banks including and , using application-layer exploits to slow websites and deny customer access, resulting in tens of millions in mitigation costs. The U.S. Department of Justice indicted seven individuals affiliated with Iran's (IRGC) in 2016 for orchestrating these via leased botnets, motivated by retaliation for Western sanctions and alleged covert operations like ; the attacks exemplified state use of cyber proxies to impose asymmetric pressure on financial systems without direct military confrontation. During the 2022 Russia-Ukraine conflict, both sides escalated DDoS operations for tactical disruption, with Russian actors targeting Ukrainian government and banking sites in January 2022 using multi-vector floods to precede the invasion, aiming to hinder coordination and information flow. Ukrainian groups retaliated with DDoS against Russian infrastructure, including United Russia party domains, highlighting DDoS as a supplementary tool in hybrid warfare; while Russian operations showed state-level sophistication through integrated wiper malware and floods, attribution relied on malware signatures and timing, underscoring persistent challenges in proving direct government command amid deniable volunteer networks.

Attack Methodologies

Volumetric Flooding Techniques

Volumetric flooding techniques in denial-of-service attacks target the exhaustion of a victim's inbound bandwidth by generating and directing overwhelming quantities of toward the network link, rendering it incapable of handling legitimate flows due to saturation-induced congestion and . These methods operate primarily at OSI layers 3 and 4, focusing on raw packet volume rather than protocol state manipulation or application exploitation, which distinguishes them from other DDoS categories. The causal mechanism relies on the finite capacity of pipes—typically measured in gigabits or terabits per second—where attacker-generated exceeds this threshold, forcing routers to drop packets indiscriminately. Direct flooding variants include UDP floods and ICMP floods, which leverage simple, stateless protocols for high-volume packet generation. A UDP flood involves dispatching vast numbers of packets to arbitrary ports on the target IP, exploiting UDP's lack of connection establishment to spoof sources easily; the recipient, finding no listening service, responds with ICMP port-unreachable messages, further taxing resources and bandwidth. This technique has been documented since early DDoS tools, with attackers using botnets to scale output to hundreds of Gbps. Similarly, an ICMP flood—commonly known as a —saturates the target with ICMP echo-request packets, compelling replies that double the traffic if unmitigated, though modern systems often limit responses to prevent abuse. Amplification attacks enhance volumetric efficacy by exploiting protocols with disproportionate request-response sizes, reflecting and magnifying traffic via third-party servers misconfigured for . In DNS amplification, attackers spoof the victim's IP in queries to recursive resolvers for voluminous records (e.g., DNS ANY queries), prompting responses up to 50 times larger directed at the spoofed address; this method fueled attacks exceeding 100 Gbps as early as 2013. NTP amplification abuses vulnerable servers, such as through monlist queries exposing large client lists, achieving factors over 500x amplification and powering a 400 Gbps assault in 2014. These reflection-based tactics minimize attacker bandwidth needs while maximizing victim impact, often combining with botnets for distribution. Real-world deployments underscore the scalability of volumetric techniques, with botnets like Mirai enabling peaks such as the 1.2 Tbps UDP-based flood against DNS provider Dyn on October 21, 2016, disrupting major sites including and . Subsequent incidents include a 2.54 Tbps attack on Google Cloud in 2017, mitigated without outage, highlighting defenses' role yet affirming volumetric threats' persistence into the 2020s, where hyper-volumetric assaults routinely surpass 1 Tbps via amplified UDP vectors.

Protocol Exploitation Methods

Protocol exploitation methods in denial-of-service (DoS) attacks target inherent behaviors or vulnerabilities in network protocols, such as TCP/IP and ICMP, to exhaust server resources, disrupt connections, or trigger system failures without necessarily requiring massive traffic volumes. These techniques manipulate protocol states or amplification mechanisms, forcing the target to allocate memory, processing power, or bandwidth inefficiently. Unlike volumetric floods, protocol exploits often succeed by abusing legitimate protocol logic, such as connection handshakes or packet fragmentation, making them stealthier and harder to distinguish from normal traffic. A prominent example is the SYN flood attack, which exploits the three-way handshake in the Transmission Control Protocol (TCP). An attacker sends numerous SYN packets to initiate connections but spoofs the source IP or fails to respond to the server's SYN-ACK packets, leaving half-open connections that consume server memory and backlog queues. Each unfinished handshake ties up resources until timeouts expire, typically 3-4 minutes per entry, potentially exhausting the server's connection table of 1,000-65,000 slots depending on configuration. First documented in the early 1990s and notably used by , SYN floods overwhelmed systems like ts servers in 1994-1995 incidents, rendering them unresponsive to legitimate requests. Modern variants may use distributed sources to evade . ICMP-based exploits, such as the , abuse the (ICMP) by sending oversized or malformed ping (echo request) packets that exceed the 65,535-byte IPv4 maximum when fragmented and reassembled. Targets crash due to buffer overflows during reassembly, as early implementations failed to validate packet sizes properly. Prevalent in 1996 against Windows and Unix systems, this attack was mitigated by patches enforcing fragment checks, rendering it ineffective against updated software but illustrative of protocol fragmentation flaws. Amplification attacks like the Smurf exploit IP broadcast addressing and ICMP echo replies. The attacker spoofs the victim's IP as the source and directs ICMP echo requests to a subnet's , prompting all hosts (potentially hundreds) to reply with larger echo replies to the victim, multiplying traffic by the network size factor. Emerging around 1997, Smurf attacks peaked in 1998, causing widespread outages; for instance, a 1998 incident amplified a small request into gigabits of response . Mitigation involves disabling broadcast responses on routers, as recommended by RFC 2644 in 1999. A UDP variant, Fraggle, substitutes UDP for ICMP to bypass some filters, achieving similar amplification. Other protocol exploits include the Teardrop attack, which sends overlapping IP fragments that confuse reassembly routines, leading to kernel panics in unpatched systems like in 1997. These methods underscore how protocols designed for reliability can be inverted for disruption, with effectiveness declining against hardened implementations but persisting in botnet-orchestrated forms.

Application-Layer and Behavioral Attacks

Application-layer denial-of-service attacks target the seventh layer of the , focusing on web applications and protocols such as HTTP/HTTPS to exhaust resources like CPU cycles, memory, and database connections. These attacks generate traffic that closely resembles legitimate user activity, complicating detection and mitigation compared to volumetric or protocol-based methods, as they evade many network-level filters. Attackers often leverage botnets or scripted tools to issue requests that trigger resource-intensive operations, such as dynamic page generation or calls, thereby amplifying impact with minimal bandwidth. A primary mechanism is the HTTP flood, where attackers inundate a server with high volumes of GET or POST requests, each appearing valid but collectively overwhelming capacity. For instance, requests may target specific endpoints that invoke computationally expensive tasks, like search functions or checks, leading to degraded response times or complete service denial. Such floods can achieve denial through sheer request-per-second rates, with defenses relying on behavioral analysis to identify anomalies in request patterns, such as unnatural uniformity in user agents or referrers. Behavioral variants, often termed low-and-slow attacks, exploit application tolerance for prolonged connections by sending incomplete or dribbled data packets, tying up server threads without generating detectable traffic spikes. The Slowloris technique exemplifies this: an attacker establishes multiple partial HTTP connections, periodically injecting minimal header bytes to prevent timeouts while avoiding full request completion. This method, executable from a single machine with low bandwidth—typically under 1 Mbps—can exhaust connection pools on servers like configured with limited worker threads. Similar approaches include R.U.D.Y. (R U Dead Yet), which slowly transmits body data to mimic file uploads, further straining stateful application handling. These attacks underscore causal dependencies in application design, where per-connection resource allocation enables asymmetric exhaustion, as a handful of sustained sessions can block hundreds of legitimate ones. By 2026, application-layer attacks have increasingly incorporated AI-powered precision targeting with real-time adaptation, focusing on API endpoints and workflows such as logins and searches, enhancing their stealth and effectiveness. Multi-vector attacks combining these with protocol methods like SYN floods and Slowloris further complicate detection. Detection challenges arise from the attacks' stealth, necessitating application-level scrutiny of metrics like connection duration, request completeness, and entropy in traffic signatures. Mitigation typically involves , connection timeouts, and firewalls that enforce request validation, though evasion tactics—such as varying inter-packet delays or spoofing diverse client behaviors—persist as attackers adapt to countermeasures. Empirical data from cybersecurity reports indicate application-layer attacks comprised approximately 20-30% of DDoS incidents in recent years, rising due to the proliferation of IoT botnets capable of sophisticated request crafting.

Distributed and Amplified Variants

Distributed denial-of-service (DDoS) attacks extend single-source DoS by coordinating traffic from numerous compromised devices, forming a of "zombies" or agents controlled via command-and-control (C2) servers. This distribution complicates mitigation, as traffic appears from diverse IP addresses, overwhelming targets through sheer volume rather than a traceable origin. Early examples include the Stacheldraht tool, deployed in 1999, which used handler servers to direct agents in flooding attacks. Modern botnets like Mirai, emerging in 2016, exploited vulnerable IoT devices such as cameras and routers, infecting over 100,000 devices to launch attacks peaking at hundreds of Gbps. The Mirai release amplified its spread, enabling variants that powered assaults like the October 2016 Dyn attack, disrupting major sites via 1.2 Tbps of traffic. Emerging trends by 2026 feature sophisticated botnets with AI optimization for high-frequency strikes, traffic camouflage to evade detection, and use of DDoS as smokescreens for deeper intrusions, often in multi-vector combinations. Amplification variants leverage protocols with asymmetric request-response sizes to magnify impact, spoofing the target's IP to reflect large responses from third-party servers. In DNS amplification, attackers send small queries to open resolvers, eliciting responses up to 50 times larger directed at the victim; a 2013 attack using this method reached 300 Gbps. NTP amplification exploits monlist commands on vulnerable servers for factors exceeding 200x, as seen in 2014 attacks hitting 400 Gbps. SSDP amplification, targeting UPnP services, achieved factors around 30x in assaults like the 2014 exploit of multimedia devices. More recent cases include 2018 Memcached attacks amplifying to 1.2 Tbps by abusing key-value stores with up to 50,000x factors. These techniques combine with botnets for hybrid threats, where distributed sources query amplifiers to evade detection and scale volume efficiently.

Detection and Immediate Effects

Symptoms and Indicators

Denial-of-service (DoS) attacks typically manifest as degraded or complete unavailability of targeted services, often indistinguishable from non-malicious network overloads or hardware failures without deeper analysis. Users may experience slow loading times for websites or applications, delays in file transfers or streaming, and frequent connection timeouts, such as HTTP 504 gateway errors. Key indicators include sudden spikes in inbound traffic volumes, which can exceed normal baselines by orders of magnitude, often originating from unusual geographic locations or ranges inconsistent with typical user patterns. tools may reveal elevated latency, , or resource exhaustion, such as CPU utilization approaching 100% due to processing flood requests or incomplete handshakes in variants. System logs often show patterns like repeated failed attempts, anomalous protocol behaviors (e.g., malformed packets in ICMP floods), or a surge in half-open connections overwhelming server queues. Application-layer indicators include disproportionate requests to specific endpoints, mimicking legitimate user agents but at unsustainable rates, leading to or overloads. Early detection relies on baselining normal traffic and alerting on deviations, such as bandwidth saturation or error rate spikes in real-time dashboards, enabling rapid to differentiate attacks from organic surges.

Performance and Systemic Impacts

Denial-of-service attacks degrade target system by exhausting critical resources, including network bandwidth, CPU cycles, and memory, which elevates latency, diminishes packet throughput, and can culminate in total service unavailability. Volumetric flooding overwhelms inbound connections, triggering buffer overflows and selective packet discards, while protocol-based methods, such as SYN floods, saturate connection tables, preventing legitimate session establishment. Application-layer attacks, by contrast, mimic valid requests to monopolize server processing, resulting in response times ballooning from milliseconds to seconds or outright failures. Quantifiable examples underscore these effects: the October 21, 2016, attack on Dyn's DNS infrastructure, leveraging the Mirai botnet and peaking above 1.2 Tbps, disrupted access to major sites including , , and for up to 12 hours in and , with affected domains experiencing near-100% outage rates during peaks. The February 2018 GitHub incident, reaching 1.35 Tbps via Memcached amplification, caused brief but severe degradation, forcing reliance on upstream scrubbing to avert prolonged downtime despite handling over 100 million packets per second. Even sub-terabit assaults, as observed in Cloudflare's 2024 reports, routinely halve effective bandwidth and double latency for unmitigated targets. Systemically, DoS attacks propagate strain to intermediary networks, where ingress traffic floods induce peering congestion and backpressure, impairing unrelated services and elevating error rates across ISP backbones. Reflected amplification variants exacerbate this by generating disproportionate outbound responses from innocent servers, compounding load on global routing fabrics and occasionally triggering autonomous throttling of innocent traffic. In densely interconnected ecosystems like cloud providers, such overflows have historically cascaded to multi-tenant environments, amplifying downtime scopes beyond the primary victim and hindering recovery through shared resource contention.

Defensive Strategies

Network-Level Protections

Network-level protections against denial-of-service (DoS) attacks primarily target volumetric and protocol-based threats at layers 3 and 4 of the by filtering, diverting, or dropping malicious traffic at the routing or edge infrastructure, preventing it from overwhelming downstream resources. These methods leverage internet routing protocols like (BGP) and infrastructure-scale capacity to absorb or nullify floods before they impact applications or hosts. One common technique is remotely triggered black holing (RTBH), which uses BGP announcements to redirect traffic destined for attacked prefixes to a null interface, effectively discarding it en route. This approach, deployable by network operators or ISPs, mitigates high-volume floods by advertising a more specific route with a blackhole attribute (e.g., 0xFFFF029A), ensuring packets are dropped without consuming victim bandwidth. RTBH proved effective during the 2016 Dyn DNS attack, where operators blackholed affected anycast prefixes to limit propagation, though it indiscriminately blocks legitimate traffic to the same prefix, necessitating careful prefix engineering to minimize collateral disruption. Traffic scrubbing centers provide a selective alternative by rerouting suspect traffic via BGP to specialized facilities equipped with high-capacity hardware for and . These centers classify and filter packets—dropping malformed or excessive flows while forwarding cleaned legitimate traffic back to the origin—scaling to terabit-per-second volumes through distributed scrubbing nodes. For instance, during the 2020 AWS-hosted attacks peaking at 2.3 Tbps, scrubbing diverted and scrubbed inbound floods, restoring service without full blackholing. Limitations include latency from rerouting (typically 10-50 ms) and dependency on upstream ISP cooperation for diversion. Anycast routing enhances resilience by mapping a single IP prefix to multiple geographically dispersed sites, automatically distributing incoming traffic via BGP's shortest-path selection and diluting attack concentration on any single node. This method confines volumetric DoS to regional subsets, as seen in DNS root servers absorbing multi-gigabit floods since the early 2000s without outage, by shifting load dynamically during surges. However, sophisticated attackers may target all instances, requiring hybrid use with filtering to address state-exhaustion variants. Additional network-level measures include rate limiting at edge routers to cap packets per second or flows per interface, thwarting amplification exploits like DNS reflection, and BGP Flowspec for propagating real-time filtering rules across autonomous systems to block specific attack signatures upstream. These techniques, often automated via monitoring tools detecting anomalies like sudden entropy drops in source IPs, integrate with ISP-level peering to enforce at scale, though evasion via IP spoofing demands ongoing signature updates. Empirical data from 2023-2024 incidents show combined routing and scrubbing reducing mitigation times from hours to minutes when pre-configured.

Application and Host-Based Mitigations

Application and host-based mitigations target denial-of-service (DoS) attacks by implementing defenses directly within the affected software applications or on the host operating system, focusing on , request validation, and behavioral filtering to preserve without relying solely on upstream network infrastructure. These approaches are particularly effective against application-layer attacks, such as HTTP floods or slow-rate exploits, where malicious traffic mimics legitimate requests to exhaust server-side resources like CPU, memory, or database connections. Unlike network-level scrubbing, host-based methods allow fine-grained control but require careful tuning to avoid impacting genuine users, as over-aggressive filtering can introduce false positives. Rate limiting stands as a foundational technique, enforcing caps on the frequency of requests from individual IP addresses, user sessions, or endpoints within defined time windows, such as allowing no more than 100 requests per minute per IP to thwart volumetric application-layer floods. Algorithms like or enable dynamic enforcement, discarding excess traffic while permitting bursts of legitimate activity; for instance, web servers such as can configure modules like limit_req to apply these limits, reducing the risk of server overload from automated bots. Geolocation-based or behavioral further refines this by adjusting thresholds for suspicious patterns, though attackers may evade IP-based limits via proxies or distributed botnets. At the host level, TCP SYN cookie mechanisms mitigate attacks by encoding connection state in the SYN-ACK response rather than allocating server memory for unverified half-open connections, a method standardized in kernels since version 2.2 and effective against memory-exhaustion variants without requiring additional hardware. Tools like Fail2Ban monitor logs for anomalous patterns—such as repeated failed logins or malformed packets—and dynamically update host firewalls (e.g., ) to block offending IPs, with configurable ban durations starting at minutes and escalating based on violation severity. Input sanitization and resource quotas, enforced via application code or OS controls like cgroups, prevent exploits such as Slowloris by validating request completeness and limiting per-process CPU or memory usage, ensuring no single thread monopolizes resources. CAPTCHA or proof-of-work challenges impose computational costs on clients, verifying human interaction for sensitive endpoints like forms, thereby deterring scripted application-layer DoS attempts; however, their efficacy diminishes against sophisticated attackers employing CAPTCHA-solving services or in scenarios where connection establishment precedes the challenge. Web application firewalls (WAFs) deployed host-side, such as , apply rule sets to inspect and block anomalous HTTP behaviors—like irregular headers or URI lengths—before they reach the application core, with signature-based detection for known attack vectors complementing anomaly thresholds. These mitigations demand ongoing monitoring and updates, as evolving attack techniques, including those leveraging for evasion, necessitate adaptive configurations to maintain effectiveness.

Advanced and Emerging Defenses

and techniques have advanced DDoS detection by analyzing vast datasets for anomalous patterns that traditional rule-based systems overlook. models, such as convolutional neural networks (CNNs) integrated with Visual Geometry Group architectures, achieve high accuracy in identifying DDoS traffic across networks by processing packet flows and behavioral signatures in real-time. Recurrent neural networks (RNNs), (LSTM) units, and gated recurrent units (GRUs) excel in sequential , with comparative studies showing Bi-LSTMs outperforming others in precision for application-layer attacks, reaching detection rates above 99% in controlled datasets from 2025 evaluations. Hyperparameter tuning and hybrid models further enhance adaptability, as demonstrated in cloud environments where optimized ML classifiers reduce false positives by integrating flow-based features like traffic representations. Akamai's Behavioral DDoS Engine, deployed commercially since 2024, uses to baseline legitimate traffic and dynamically block evolving attack vectors, mitigating multi-vector assaults peaking at hundreds of gigabits per second. Predictive mitigation powered by AI automates responses to -orchestrated attacks, which surged in sophistication post-2023 by leveraging generative models for coordination. Real-time via neural networks enables proactive scrubbing, where systems like those from Radware forecast attack escalations and reroute traffic through global networks, absorbing volumes exceeding 1 terabit per second as observed in 2025 incidents. These approaches counter the asymmetry where attackers use AI for adaptive flooding, by employing similar computational power for defensive and automated policy enforcement, though limitations persist in zero-day exploits requiring continuous model retraining. Moving target defense (MTD) paradigms shift static vulnerabilities by dynamically reconfiguring network elements, such as proxy remapping or service relocation, to frustrate attacker and sustainment phases. In proxy-based architectures, periodic proxy replacement and client reassignment have empirically reduced DDoS efficacy by increasing targeting costs, with game-theoretic models from 2016-2025 simulations showing defender utility gains of up to 40% against persistent floods. Adaptive MTD for industrial IoT, introduced in 2025 studies, integrates to shuffle IP addresses and ports, restoring service availability under volumetric assaults that traditional static defenses fail. Cost-effective implementations balance overhead by shuffling only during detected threats, preserving performance in settings. Emerging collaborative frameworks incorporate for decentralized attack intelligence sharing, enabling SDN-orchestrated mitigation without central trust points. Lightweight protocols, tested on testnets in 2025, facilitate real-time DDoS signal propagation across providers, enhancing scalability for distributed defenses against amplified variants. While promising for incentivized peer reporting, 's latency in high-throughput scenarios limits it to supplementary roles, with empirical prototypes showing 20-30% faster collective response times over siloed systems. These defenses collectively address post-2023 trends of hyper-volumetric and geopolitically motivated attacks, emphasizing proactive, data-driven resilience over reactive filtering.

Broader Consequences

Economic and Operational Costs

Denial-of-service (DoS) attacks impose substantial economic burdens on victims through direct revenue losses from service disruptions and indirect expenses such as mitigation and recovery efforts. The average cost of a DDoS attack reached approximately $408,000 in 2023 for affected organizations, primarily driven by an average of 68 minutes at a rate of $6,000 per minute of interruption. For small-to-medium-sized businesses, per-incident costs average $52,000, while enterprises face around $444,000, reflecting scaled dependencies on online operations. These figures encompass not only immediate but also post-attack forensics and infrastructure hardening, which can extend financial strain beyond the attack duration. Operational costs further compound the impact, diverting internal resources toward incident response and straining IT teams. Large businesses experience annual global downtime costs from DDoS-related IT disruptions estimated at $400 billion, attributable to factors like emergency bandwidth provisioning and staff overtime. In sectors reliant on real-time services, such as e-commerce and finance, attacks halt transaction processing, leading to cascading effects like inventory backlogs and customer churn; for instance, a 45-minute attack can tally $270,000 in losses at $6,000 per minute. Reputational damage amplifies operational challenges, as prolonged outages erode trust and necessitate marketing campaigns for recovery, with surveys indicating average total costs nearing $500,000 when including these intangibles. Costs vary by attack scale and preparedness, with unprotected entities bearing higher per-minute expenses—up to $120,000 for some SMEs—due to inefficient reactive measures like manual rerouting. Financial institutions, increasingly targeted via application-layer attacks (up 23% from 2023 to 2024), face amplified operational disruptions from vulnerabilities, potentially escalating mitigation needs through specialized scrubbing services. Empirical analyses of over 300,000 attacks from 2023 to mid-2025 underscore that while individual incidents may appear low-impact, cumulative effects on network stability drive ongoing investments in resilience, often exceeding initial attack expenditures.

Unintentional DoS and Collateral Damage

Unintentional denial-of-service (DoS) effects stem from non-adversarial causes, primarily legitimate traffic surges or system flaws that exhaust resources without malicious intent. Flash crowds, characterized by abrupt spikes in genuine user requests, exemplify this phenomenon; a 2002 analysis of web server logs identified such events as causing server loads to increase dramatically, straining networks and delaying responses for all users. These differ from deliberate attacks by originating from uncoordinated, high-demand activities like viral news coverage or promotional launches, yet produce indistinguishable overload symptoms, complicating discrimination without advanced traffic analysis. Software defects or misconfigurations further contribute to unintentional DoS. For example, programming errors in applications can trigger infinite loops or excessive resource consumption, leading to host overload; a gloss on DoS variants notes that such flaws in code execution mimic attack-induced exhaustion by amplifying minor inputs into systemic failures. A stark real-world case occurred on July 19, 2024, when a faulty kernel driver update in CrowdStrike's Falcon sensor software caused up to 8.5 million Windows devices worldwide to enter boot-loop states, resulting in cascading outages across aviation, healthcare, and financial sectors—equating to unintentional DoS impacts far exceeding typical targeted disruptions. Collateral damage from intentional DoS attacks extends harm beyond primary targets, affecting third parties through shared or indirect consequences. A 2018 Neustar survey of cybersecurity professionals found that 27% of DDoS-impacted organizations believed they were unintended victims, often due to attacks on co-located services or upstream providers spilling over via bandwidth saturation. Amplification techniques, such as DNS reflection, generate backscatter traffic that burdens innocent networks; this unintended flood can degrade performance for uninvolved ISPs and endpoints, as observed in volumetric campaigns where spoofed queries elicit oversized responses routed indiscriminately. Such spillover effects compound during multi-target or infrastructural assaults. Kaspersky's 2015 analysis of DDoS incidents revealed that 26% resulted in sensitive for victims, not through direct exfiltration but via collateral chaos—rushed mitigations, insider errors under pressure, or opportunistic exploits amid the disorder. In environments, attacks on one tenant's resources can shared pools, inadvertently denying service to unrelated applications; Akamai reported in 2023 that such shared-space vulnerabilities amplified downtime across ecosystems during 2022's surge in application-layer DDoS. These dynamics underscore how DoS tactics, even when precisely aimed, propagate externalities, eroding resilience in interconnected digital infrastructures.

Backscatter and Network-Wide Effects

In denial-of-service (DoS) attacks employing IP spoofing, arises as third-party systems respond to forged packets, directing unsolicited replies toward the spoofed source addresses, which may include innocent or unused IP ranges. This side-effect facilitates passive monitoring of attacks via telescopes or unused address blocks, where incoming backscatter packets reveal attack characteristics such as volume, duration, and protocol usage without requiring victim cooperation. For example, cooperative Association for Investments in (CAIDA) measurements from 1997 to 2000 captured backscatter indicative of thousands of attacks, enabling inference of global DoS activity through statistical analysis of response patterns like TCP SYN-ACKs or ICMP echoes. Backscatter analysis has consistently documented high attack frequencies; between February 2001 and May 2004, roughly 2,000 to 3,000 distinct DoS incidents were detected weekly via this method, with many involving spoofed floods exceeding 100 Mbps in inferred bandwidth. Such data underscores spoofing's role in evading source accountability, as attackers leverage public servers for reflection, amplifying traffic by factors of 50 or more in protocols like DNS or NTP, where query responses vastly outsize requests. Network-wide effects of compound the targeted disruption, as diffused response traffic burdens upstream Internet Service Providers (ISPs) and intermediate routers with extraneous load, potentially degrading latency and throughput for unrelated traffic. In volumetric attacks, this scattered —often comprising 1-5% of total reflected volume when spoofing is randomized—can saturate shared links, triggering congestion that affects regional or national segments; for instance, early 2000s observations linked backscatter spikes to ISP-level anomalies, where innocent endpoints absorbed gigabits of unintended replies, mimicking secondary DoS conditions. These externalities extend to operational challenges for ISPs, who must deploy ingress filtering or traffic scrubbing to contain spillover, yet incomplete adoption leaves vulnerabilities: unmitigated backscatter has been tied to broader outages, as seen in analyses of attacks saturating provider backbones and causing collateral packet loss rates up to 10-20% during peaks. Moreover, amplification variants exacerbate this by directing outsized replies across diverse AS paths, inflating global routing table pressures and increasing the risk of cascading failures in under-provisioned networks. Empirical datasets from backscatter monitoring confirm that over 80% of detected attacks in sampled periods involved protocols prone to such diffusion, highlighting the inherent scalability costs imposed on the internet's collective infrastructure.

Criminalization and Prosecution

In the United States, denial-of-service (DoS) attacks, including distributed variants, are criminalized primarily under the Computer Fraud and Abuse Act (CFAA), enacted in 1986 as 18 U.S.C. § 1030. This statute prohibits intentional access to protected computers without authorization or exceeding authorized access, resulting in damage or impairment, which encompasses actions that overload systems and deny service. Violations can lead to felony charges with penalties including fines and imprisonment up to 10 years for first offenses causing significant damage, escalating for repeat offenses or those involving critical infrastructure. Prosecutions under the CFAA have targeted both direct perpetrators and operators of DDoS-for-hire services, known as booters or stressers. In December 2024, U.S. authorities charged two defendants as part of a global operation seizing 27 domains linked to leading booter services, applying CFAA provisions to those enabling attacks via rented s. Earlier, in August 2025, an man faced charges for administering the "Rapper Bot" DDoS-for-hire , which launched attacks averaging 2-3 terabits per second against victims in over 80 countries. The (FBI) has emphasized that participating in or providing such services constitutes a federal offense, with ongoing international partnerships to dismantle networks. Internationally, DoS attacks are prohibited under various national laws harmonized by frameworks like the Council of Europe's Convention on Cybercrime (Budapest Convention), ratified by over 60 countries since 2001, which requires criminalizing intentional impairment of computer data or systems. In the , the , as amended, deems unauthorized acts impairing electronic communications—including DoS overloads—criminal offenses punishable by up to 10 years imprisonment. Canadian authorities prosecute under the provisions against mischief to computer data, as seen in early cases like the 2000 "Mafiaboy" attacks on major websites, where the perpetrator received eight months in open custody and a year of probation. Enforcement often involves cross-border cooperation, though jurisdictional hurdles persist.

Challenges in Attribution and International Law

Attributing the perpetrators of denial-of-service (DoS) attacks presents significant technical and evidentiary hurdles, primarily due to the distributed nature of these operations. Attackers frequently employ botnets comprising thousands of compromised devices worldwide, spoofed IP addresses, and amplification techniques that obscure the command-and-control infrastructure, rendering forensic traceback to the true origin exceedingly difficult even for advanced defenders. The volumetric surge generated by such methods further dilutes signals of the attack's source, complicating real-time analysis and post-incident investigation. In cases involving state actors, additional layers of proxies, false-flag operations, and exacerbate these issues, as non-state proxies or criminal elements may be leveraged to maintain separation from sponsoring entities. Historical examples underscore these attribution gaps. The 2007 DDoS attacks on Estonian government and financial websites, which paralyzed online services for days, were linked by Estonian authorities and to Russian state-affiliated hackers amid geopolitical tensions over a Soviet-era monument relocation, yet definitive legal attribution to the Russian government eluded international bodies due to insufficient chain-of-custody . Similarly, the 2016 Mirai botnet-fueled assaults on Dyn DNS disrupted major U.S. sites like and , but while the malware's authors were arrested, broader orchestration—potentially involving state tolerance of infrastructure—remained unproven, highlighting how compromised IoT devices from neutral third countries hinder . These cases illustrate that while technical indicators like code signatures or timing correlations can suggest actors, establishing intent and control for legal purposes often falters without or cooperative foreign disclosures. Under , these attribution challenges intersect with frameworks for and the , as codified in instruments like the UN Charter and the International Law Commission's Articles on . For a DoS attack to trigger countermeasures such as under Article 51, it must qualify as an "armed attack," a threshold rarely met by non-destructive DDoS operations that merely overwhelm capacity without physical damage or loss of life; the on the International Law Applicable to Cyber Operations posits that equivalent effects could suffice, but lacks binding consensus and has not resolved disputes over volumetric floods. Attribution to a state requires demonstrating that the act was directed or controlled by its organs or agents, yet cyber operations' anonymity permits governments to exploit non-attributable actors, invoking and evading responsibility under . Absent specialized treaties—none exist explicitly for DoS—these attacks often fall into a gray zone below armed conflict, limiting responses to diplomatic protests or sanctions, which prove ineffective without verifiable proof. Prosecution faces parallel barriers, as domestic laws like the U.S. criminalize DoS but require international cooperation for cross-border actors, where treaties may exclude cyber offenses or host states refuse handover citing . Proposals for an international attribution agency, akin to those in nuclear forensics, have surfaced to standardize evidence-sharing and reduce politicization, but geopolitical distrust—evident in mutual accusations between the U.S., , and —impedes implementation. Consequently, many DoS incidents yield no accountability, perpetuating deterrence failures and incentivizing escalation in hybrid conflict scenarios.

Policy Debates on Cyber Defense Norms

Policy debates surrounding cyber defense norms for denial-of-service (DoS) attacks center on the tension between restraint and proactive countermeasures, particularly in distinguishing defensive actions from retaliatory ones that risk escalation. Proponents of active cyber defense argue that allowing victims—especially private entities—to disrupt attackers' command-and-control infrastructure could enhance deterrence against DoS campaigns, which often overwhelm targets without physical damage but cause significant economic disruption. Critics, however, contend that such "hacking back" violates international norms by potentially enabling unauthorized intrusions into third-party networks, complicating attribution, and inviting cycles of retaliation, as DoS perpetrators frequently operate through proxies like botnets. In the United States, legislative efforts like the proposed Active Cyber Defense Certainty Act (ACDC) of 2017 sought to permit limited private-sector responses to intrusions, including tracing and disabling used in DoS attacks, but faced opposition for undermining proportional principles under and risking to uninvolved parties. The debate persists, with recent analyses in 2025 highlighting ethical dilemmas: while hack-back might neutralize immediate threats from state-sponsored or criminal DoS operations, it blurs lines between defense and offense, potentially eroding norms against peacetime cyber interference. Internationally, frameworks like the 2.0 (2017) classify severe DoS attacks as potential violations of if they interfere with a state's critical functions, but typically below the threshold of an "armed attack" justifying force, urging restraint to avoid broadening conflict. UN Group of Governmental Experts (GGE) reports emphasize voluntary norms, such as not targeting essential civilian infrastructure with DoS tactics, yet enforcement remains elusive due to attribution challenges and non-binding status, fueling debates on whether states should publicly commit to "persistent engagement" strategies that preempt DoS vectors without crossing into offense. These discussions underscore a causal gap: empirical data shows DoS attacks surged post-2020 without proportional normative deterrents, prompting calls for hybrid public-private norms that prioritize resilience over retaliation.

Surge in Attack Volumes Post-2023

Following the relative stabilization of DDoS attack frequencies in 2022–2023, volumes surged markedly from late 2023 onward, with cybersecurity firms reporting exponential increases in both attack counts and peak bitrates. documented an 83% year-over-year rise in mitigated DDoS attacks in Q4 2024, reaching 6.9 million incidents, followed by a 358% year-over-year jump in Q1 2025, where 20.5 million attacks were blocked—equivalent to 96% of the total volume for all of 2024. Similarly, Akamai observed Layer 7 (application-layer) DDoS attack volumes escalating from over 500 billion monthly events in early 2023 to more than 1.1 trillion by 2024, a 94% overall growth driven by sustained high-frequency campaigns. This uptick extended into 2025, with Gcore reporting a 41% increase in overall DDoS attack volumes from Q3–Q4 2024 to Q1–Q2 2025, including a 10% rise in application-layer incidents, totaling over 969,000 attacks in the latter period. Netscout's analysis highlighted a 360% surge in Mirai-powered attacks against service providers in 2024 alone, contributing to broader volume spikes amid geopolitical tensions. Qrator Labs noted a 43% increase in L3–L4 DDoS attacks in Q2 2025 compared to Q2 2024, underscoring the persistence of volumetric floods. Peak attack magnitudes also escalated, reflecting amplified botnet capacities and amplification techniques. mitigated a record 5.6 Tbps attack in Q4 2024, part of a trend where hyper-volumetric assaults exceeding 1 Tbps became routine by early 2025. Akamai confirmed four of its ten largest-ever DDoS mitigations occurred in 2024, with sizes surpassing prior benchmarks due to proliferated IoT botnets and AI-assisted tooling. These developments strained global network defenses, as evidenced by a 56% rise in attack volumes in H2 2024 versus H2 2023, per aggregated industry .

Shifts Toward Sophistication and Hyper-Volumetric Attacks

In recent years, distributed denial-of-service (DDoS) attacks have escalated in scale, with hyper-volumetric variants—defined as those exceeding 1 terabit per second (Tbps)—becoming markedly more prevalent. reported blocking over 6,500 such attacks in the second quarter of 2025 alone, a sharp rise attributed to amplified reflection techniques and expansive botnets leveraging unsecured internet-connected devices. This trend reflects a causal shift driven by ' access to larger, globally distributed infrastructures, enabling sustained floods that overwhelm even advanced scrubbing centers. For instance, a 7.3 Tbps attack targeted in mid-May 2025, surpassing prior benchmarks and highlighting the feasibility of multi-terabit barrages. By 2026, however, DDoS attacks shifted toward smaller, smarter, AI-enhanced methods emphasizing precision targeting with real-time adaptation over sheer volume. Key developments included increased Layer 7 and API attacks focusing on workflows such as logins and searches, alongside multi-vector assaults combining techniques like SYN floods with Slowloris to evade defenses. Attackers increasingly deployed DDoS as smokescreens for deeper intrusions, employing traffic camouflage and AI-optimized strategies that complicated detection. Parallel to volumetric intensification, DDoS tactics have trended toward greater sophistication, incorporating multi-vector strategies that blend network-layer floods with application-layer (Layer 7) exploits to evade detection. Akamai observed in 2024 that modern attacks prioritize layered complexity over sheer size, with four of the ten largest ever mitigated by their systems occurring that year, often combining volumetric amplification with targeted HTTP floods exceeding 100 million requests per second. These evolutions exploit protocol vulnerabilities, such as DNS and NTP amplification, while integrating behavioral to bypass rate-limiting and machine learning-based defenses, rendering traditional volumetric mitigations insufficient. Multi-vector attacks, which simultaneously assault multiple OSI layers, increased in prevalence post-2023, as evidenced by a 53% year-over-year rise in total incidents mitigated by in 2024. This dual shift—hyper-volumetric for brute-force saturation and sophisticated layering for persistence—stems from commoditized tools like DDoS-for-hire services, which democratize advanced capabilities to non-state actors. Arelion's 2025 threat report documented a 97% increase in average attack size and a 63% surge in peak volumes, with a 1.57 Tbps in October 2024 exemplifying how amplification factors now routinely multiply input traffic by thousands. Such developments challenge defenders, as hyper-volumetric waves serve to mask subtler application-layer intrusions, prolonging disruption; for example, Q2 2025 saw attacks blending massive UDP floods with stealthy GET/POST request surges, extending durations beyond four days in some cases. Empirical data from mitigation providers indicate that while volumetric attacks remain dominant for initial overload, sophisticated achieve higher success rates against fortified targets by adapting in real-time to countermeasures.

Role of IoT and Botnets in Modern Threats

devices, characterized by their vast numbers, persistent connectivity, and often inadequate security measures such as default credentials and unpatched firmware, have become prime targets for forming large-scale botnets used in distributed denial-of-service (DDoS) attacks. These devices, including routers, IP cameras, and smart home appliances, enable attackers to amass distributed resources capable of generating overwhelming traffic volumes, as their sheer quantity—estimated in billions globally—allows botnets to scale rapidly without relying on traditional endpoints like personal computers. The Mirai botnet exemplifies this vulnerability, emerging in 2016 by scanning for IoT devices with weak or default passwords and infecting hundreds of thousands of them, primarily security cameras and networked video recorders. This orchestrated high-profile DDoS campaigns, including a 620 Gbps assault on researcher ' website in September 2016 and the October 2016 Dyn attack that disrupted services like and by exploiting DNS infrastructure. A subsequent attack on French hosting provider OVH reached 1 Tbps, highlighting IoT botnets' capacity for terabit-scale volumetric floods. Mirai's release in 2016 spurred variants, perpetuating its influence in subsequent threats. In modern contexts, IoT botnets continue to drive escalating DDoS threats, with variants derived from Mirai and exploiting vulnerabilities in devices like wireless routers and IP cameras to launch attacks since late 2024. For instance, the botnet, active in 2024, targeted IoT flaws to build networks for DDoS-for-hire services against global IPs and cloud providers. The Aisuru botnet, leveraging IoT infections, achieved a 6.35 Tbps attack on KrebsOnSecurity in May 2025, contributing to records like a 7.3 Tbps assault later that year. Sophisticated botnets such as Kimwolf and Aisuru-Kimwolf variants enabled automated high-frequency strikes in 2026, infecting millions of devices including Android systems for distributed amplification. These , often exceeding 100,000 nodes and incorporating AI optimization, amplify attack potency through distributed amplification techniques, with global DDoS incidents averaging 880 daily in early 2025 and peaking at intensities 65% higher than prior years. The proliferation stems from manufacturers' prioritization of functionality over , enabling causal chains where unmitigated vulnerabilities lead to widespread compromise and network-level disruptions.

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