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Cyber threat hunting is a proactive search process for hidden threats in theorganization's information system. It is a crucial component of active defenseagainst advanced persistent threats (APTs). However, most of the current threathunting methods rely on Cyber Threat Intelligence(CTI), which can find knownattacks but cannot find unknown attacks that have not been disclosed by CTI. Inthis paper, we propose LogKernel, a threat hunting method based on graph kernelclustering which can effectively separates attack behaviour from benignactivities. LogKernel first abstracts system audit logs into BehaviourProvenance Graphs (BPGs), and then clusters graphs by embedding them into acontinuous space using a graph kernel. In particular, we design a new graphkernel clustering method based on the characteristics of BPGs, which cancapture structure information and rich label information of the BPGs. To reducefalse positives, LogKernel further quantifies the threat of abnormal behaviour.We evaluate LogKernel on the malicious dataset which includes seven simulatedattack scenarios and the DAPRA CADETS dataset which includes four attackscenarios. The result shows that LogKernel can hunt all attack scenarios amongthem, and compared to the state-of-the-art methods, it can find unknownattacks.

LogKernel A Threat Hunting Approach Based on Behaviour Provenance Graph and Graph Kernel Clustering