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Audit logs containing system level events are frequently used for behaviormodeling as they can provide detailed insight into cyber-threat occurrences.However, mapping low-level system events in audit logs to highlevel behaviorshas been a major challenge in identifying host contextual behavior for thepurpose of detecting potential cyber threats. Relying on domain expertknowledge may limit its practical implementation. This paper presents TapTree,an automated process-tree based technique to extract host behavior by compilingsystem events' semantic information. After extracting behaviors as systemgenerated process trees, TapTree integrates event semantics as a representationof behaviors. To further reduce pattern matching workloads for the analyst,TapTree aggregates semantically equivalent patterns and optimizesrepresentative behaviors. In our evaluation against a recent benchmark auditlog dataset (DARPA OpTC), TapTree employs tree pattern queries and sequentialpattern mining techniques to deduce the semantics of connected system events,achieving high accuracy for behavior abstraction and then Advanced PersistentThreat (APT) attack detection. Moreover, we illustrate how to update thebaseline model gradually online, allowing it to adapt to new log patterns overtime.

TapTree: Process-Tree Based Host Behavior Modeling and Threat Detection Framework via Sequential Pattern Mining