Patrick Ah-Fat and Michael Huth, Imperial College London; Rob Mead, Tim Burrell, and Joshua Neil, Microsoft
Malicious actors that have already penetrated an enterprise network will exploit access to launch attacks within that network. Credential theft is a common preparatory action for such attacks, as it enables privilege escalation or lateral movement. Elaborate techniques for extracting credentials from Windows memory have been developed by actors with advanced capabilities. The state of the art in identifying the use of such techniques is based on malware detection, which can only alert on the presence of specific executable files that are known to perform such techniques. Therefore, actors can bypass detection of credential theft by evading the static detection of malicious code. In contrast, our work focuses directly on the memory read access behaviour to the process that enforces the system security policy. We use machine learning techniques driven by data from real enterprise networks to classify memory read behaviours as malicious or benign. As we show that Mimikatz is a popular tool seen across Microsoft Defender Advanced Threat Protection (MDATP) to steal credentials, our aim is to develop a generic model that detects the techniques it employs. Our classifier is based on novel features of memory read events and the characterisation of three popular techniques for credential theft. We integrated this classifier in a detector that is now running in production and is protecting customers of MDATP. Our experiments demonstrate that this detector has excellent false negative and false positive rates, and does alert on true positives that previous detectors were unable to identify.
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