voyagar/mitre_cit_v14 huggingface dataset:
T1498.001: Network Denial of Service: Direct Network Flood
Tactics: Impact
Platforms: Azure AD, Google Workspace, IaaS, Linux, Office 365, SaaS, Windows, macOS
Sub-technique of: T1498

Adversaries may attempt to cause a denial of service (DoS) by directly sending a high-volume of network traffic to a target. This DoS attack may also reduce the availability and functionality of the targeted system(s) and network. Direct Network Floods are when one or more systems are used to send a high-volume of network packets towards the targeted service's network. Almost any network protocol may be used for flooding. Stateless protocols such as UDP or ICMP are commonly used but stateful protocols such as TCP can be used as well. Botnets are commonly used to conduct network flooding 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 distributed DoS (DDoS), so many systems are used to generate the flood that each one only needs to send out a small amount of traffic to produce enough volume to saturate the target network. In such circumstances, distinguishing DDoS traffic from legitimate clients becomes exceedingly difficult. Botnets have been used in some of the most high-profile DDoS flooding attacks, such as the 2012 series of incidents that targeted major US banks.

Detection: Detection of a network flood can sometimes be achieved before the traffic volume is sufficient to cause impact to the availability of the service, but such response time typically requires very aggressive monitoring and responsiveness or services provided by an upstream network service provider. Typical network throughput monitoring tools such as netflow, SNMP, and custom scripts can be used to detect sudden increases in network or service utilization. Real-time, automated, and qualitative study of the network traffic can identify a sudden surge in one type of protocol can be used to detect a network flood event as it starts. Often, the lead time may be small and the indicator of an event availability of the network or service drops. The analysis tools mentioned can then be used to determine the type of DoS causing the outage and help with remediation.
Data sources: Network Traffic: Network Traffic Flow, Sensor Health: Host Status
T1498.002: Network Denial of Service: Reflection Amplification
Tactics: Impact
Platforms: Azure AD, Google Workspace, IaaS, Linux, Office 365, SaaS, Windows, macOS
Sub-technique of: T1498

Adversaries may attempt to cause a denial of service (DoS) by reflecting a high-volume of network traffic to a target. This type of Network DoS takes advantage of a third-party server intermediary that hosts and will respond to a given spoofed source IP address. This third-party server is commonly termed a reflector. An adversary accomplishes a reflection attack by sending packets to reflectors with the spoofed address of the victim. Similar to Direct Network Floods, more than one system may be used to conduct the attack, or a botnet may be used. Likewise, one or more reflectors may be used to focus traffic on the target. This Network DoS attack may also reduce the availability and functionality of the targeted system(s) and network. Reflection attacks often take advantage of protocols with larger responses than requests in order to amplify their traffic, commonly known as a Reflection Amplification attack. Adversaries may be able to generate an increase in volume of attack traffic that is several orders of magnitude greater than the requests sent to the amplifiers. The extent of this increase will depending upon many variables, such as the protocol in question, the technique used, and the amplifying servers that actually produce the amplification in attack volume. Two prominent protocols that have enabled Reflection Amplification Floods are DNS and NTP, though the use of several others in the wild have been documented. In particular, the memcache protocol showed itself to be a powerful protocol, with amplification sizes up to 51,200 times the requesting packet.

Detection: Detection of reflection amplification can sometimes be achieved before the traffic volume is sufficient to cause impact to the availability of the service, but such response time typically requires very aggressive monitoring and responsiveness or services provided by an upstream network service provider. Typical network throughput monitoring tools such as netflow, SNMP, and custom scripts can be used to detect sudden increases in network or service utilization. Real-time, automated, and qualitative study of the network traffic can identify a sudden surge in one type of protocol can be used to detect a reflection amplification DoS event as it starts. Often, the lead time may be small and the indicator of an event availability of the network or service drops. The analysis tools mentioned can then be used to determine the type of DoS causing the outage and help with remediation.
Data sources: Network Traffic: Network Traffic Flow, Sensor Health: Host Status
T1496: Resource Hijacking
Tactics: Impact
Platforms: Containers, IaaS, Linux, Windows, macOS

Adversaries may leverage the resources of co-opted systems to complete resource-intensive tasks, which may impact system and/or hosted service availability. One common purpose for Resource Hijacking is to validate transactions of cryptocurrency networks and earn virtual currency. Adversaries may consume enough system resources to negatively impact and/or cause affected machines to become unresponsive. Servers and cloud-based systems are common targets because of the high potential for available resources, but user endpoint systems may also be compromised and used for Resource Hijacking and cryptocurrency mining. Containerized environments may also be targeted due to the ease of deployment via exposed APIs and the potential for scaling mining activities by deploying or compromising multiple containers within an environment or cluster. Additionally, some cryptocurrency mining malware identify then kill off processes for competing malware to ensure it’s not competing for resources. Adversaries may also use malware that leverages a system's network bandwidth as part of a botnet in order to facilitate Network Denial of Service campaigns and/or to seed malicious torrents. Alternatively, they may engage in proxyjacking by selling use of the victims' network bandwidth and IP address to proxyware services.

Detection: Consider monitoring process resource usage to determine anomalous activity associated with malicious hijacking of computer resources such as CPU, memory, and graphics processing resources. Monitor for suspicious use of network resources associated with cryptocurrency mining software. Monitor for common cryptomining software process names and files on local systems that may indicate compromise and resource usage.
Data sources: Command: Command Execution, File: File Creation, Network Traffic: Network Connection Creation, Network Traffic: Network Traffic Flow, Process: Process Creation, Sensor Health: Host Status