Adversaries may perform Endpoint Denial of Service (DoS) attacks to degrade or block the availability of services to users. Endpoint DoS can be performed by exhausting the system resources those services are hosted on or exploiting the system to cause a persistent crash condition. Example services include websites, email services, DNS, and web-based applications. Adversaries have been observed conducting DoS attacks for political purposes and to support other malicious activities, including distraction, hacktivism, and extortion.
An Endpoint DoS denies the availability of a service without saturating the network used to provide access to the service. Adversaries can target various layers of the application stack that is hosted on the system used to provide the service. These layers include the Operating Systems (OS), server applications such as web servers, DNS servers, databases, and the (typically web-based) applications that sit on top of them. Attacking each layer requires different techniques that take advantage of bottlenecks that are unique to the respective components. A DoS attack may be generated by a single system or multiple systems spread across the internet, which is commonly referred to as a distributed DoS (DDoS).
To perform DoS attacks against endpoint resources, several aspects apply to multiple methods, including IP address spoofing and botnets.
Adversaries may use the original IP address of an attacking system, or spoof the source IP address to make the attack traffic more difficult to trace back to the attacking system or to enable reflection. This can increase the difficulty defenders have in defending against the attack by reducing or eliminating the effectiveness of filtering by the source address on network defense devices.
Botnets are commonly used to conduct DDoS attacks against networks and services. Large botnets can generate a significant amount of traffic from systems spread across the global internet. Adversaries may have the resources to build out and control their own botnet infrastructure or may rent time on an existing botnet to conduct an attack. In some of the worst cases for DDoS, so many systems are used to generate requests that each one only needs to send out a small amount of traffic to produce enough volume to exhaust the target's resources. In such circumstances, distinguishing DDoS traffic from legitimate clients becomes exceedingly difficult. Botnets have been used in some of the most high-profile DDoS attacks, such as the 2012 series of incidents that targeted major US banks.
For attacks attempting to saturate the providing network, see Network Denial of Service.
OnionDuke has the capability to use a Denial of Service module.
Sandworm Team temporarily disrupted service to Georgian government, non-government, and private sector websites after compromising a Georgian web hosting provider in 2019.
ZxShell has a feature to perform SYN flood attack on a host.
|M1037||Filter Network Traffic||
Leverage services provided by Content Delivery Networks (CDN) or providers specializing in DoS mitigations to filter traffic upstream from services. Filter boundary traffic by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport. To defend against SYN floods, enable SYN Cookies.
|ID||Data Source||Data Component||Detects|
|DS0015||Application Log||Application Log Content||
Monitor for third-party application logging, messaging, and/or other artifacts that may perform Endpoint Denial of Service (DoS) attacks to degrade or block the availability of services to users. In addition to network level detections, endpoint logging and instrumentation can be useful for detection. Attacks targeting web applications may generate logs in the web server, application server, and/or database server that can be used to identify the type of attack, possibly before the impact is felt. Externally monitor the availability of services that may be targeted by an Endpoint DoS.
|DS0029||Network Traffic||Network Traffic Content||
Monitor and analyze traffic patterns and packet inspection associated to protocol(s) that do not follow the expected protocol standards and traffic flows (e.g extraneous packets that do not belong to established flows, gratuitous or anomalous traffic patterns, anomalous syntax, or structure). Consider correlation with process monitoring and command line to detect anomalous processes execution and command line arguments associated to traffic patterns (e.g. monitor anomalies in use of files that do not normally initiate connections for respective protocol(s)).
|Network Traffic Flow||
Monitor network data for uncommon data flows. Processes utilizing the network that do not normally have network communication or have never been seen before are suspicious.
|DS0013||Sensor Health||Host Status||
Detection of Endpoint DoS can sometimes be achieved before the effect is sufficient to cause significant impact to the availability of the service, but such response time typically requires very aggressive monitoring and responsiveness. Monitor for logging, messaging, and other artifacts highlighting the health of host sensors (ex: metrics, errors, and/or exceptions from logging applications)