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The Glossary is description of commonly used words and features of CAR. Critical terms such as analytic are defined here.


Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK™) is a model and framework for describing the actions an adversary may take while operating within an enterprise network. The model can be used to better characterize and describe post-access adversary behavior.


An analytic describes the observable behavior generated for a TTP. It is the method by which a TTP can be identified. Its hypothesis describes the behavior expected, and the instantiation details its platform-specific implementation.


The hypothesis should generally describe why this analytic is worth making, that is, what information do you hope to gather through this analytic, and what could this information indicate? It should describe the identifiable objects or artifacts that are associated with or modified when an ATT&CK technique takes place.

Information Domain

The information domain is the top-level categorization of the analytic. It should describe the type of analytic: Is it based on anomaly detection? A specific behavior? Statistical analysis? There are five possible information domains: Host, Network, and Analytic, External, and Other.


The pseudocode is a representative example of how the analytic query would be made.

ATT&CK Detection

A summary of the ATT&CK tactic and techniques the analytic detects as well as the level of coverage.

Unit Tests

A set of commands that can be run which should trigger the analytic.

Analytic Type

The kind of mechanism this analytic uses. The following are descriptions of the analytic types.

A TTP analytic is designed to detect a certain adversary tactic, technique or procedure.
An attribution analytic is an analytic that is designed to detect actions that are unique to a single to a certain threat actor.
A posture or hygiene analytic is designed to help in the maintenance of the analytic or security infrastructure.
Situational Awareness
A situational awareness analytic provide general information on the state of the environment. Often these analytics can be useful during an investigation, (e.g. information such as login times doesn't indicate malicious activity, but when coupled with other indicators can provide much needed additional information). These types of analytics can also be helpful for monitoring the "health" of the environment (e.g. on which hosts are sensors not working).
A forensic analytic has a low SNR and is less likely to be indicative of adversary behavior but is nevertheless useful during an investigation. Several forensic analytics are anomaly-based, which can create excessive noise in a highly dynamic environment. Other analytics may also have noise as a result of admins/power users, software installations, or internal scripts. However, when related results are triggered by multiple forensic analytics, the collective probability rises. Thus, the forensics analytics reinforce each other during the investigative process.
An anomaly analytic triggers on behavior that is not normally observed. Anomalous may not be explicitly malicious but may be suspect. For example, detection of executables that have never been run before or a process using the network which does not normally use the network. Like Situational Awareness analytics, anomaly analytics don't necessarily indicate an attack.
A statistical analytic uses various statistical mechanisms to identify adversarial behavior.
An investigative analytic is an analytic that is primarily used during an investigation rather than as an alert.
A malware analytic is an analytic used to detect a specific kind of malware, such as Mimikatz.
Event Characterization
An event characterization analytic is used to characterize the output of another analytic into certain event types.