Jun Zeng (National University of Singapore), Zheng Leong Chua (Independent Researcher), Yinfang Chen (National University of Singapore), Kaihang Ji (National University of Singapore), Zhenkai Liang (National University of Singapore), Jian Mao (Beihang University)

Endpoint monitoring solutions are widely deployed in today’s enterprise environments to support advanced attack detection and investigation. These monitors continuously record system-level activities as audit logs and provide deep visibility into security incidents. Unfortunately, to recognize behaviors of interest and detect potential threats, cyber analysts face a semantic gap between low-level audit events and high-level system behaviors. To bridge this gap, existing work largely matches streams of audit logs against a knowledge base of rules that describe behaviors. However, specifying such rules heavily relies on expert knowledge. In this paper, we present Watson, an automated approach to abstracting behaviors by inferring and aggregating the semantics of audit events. Watson uncovers the semantics of events through their usage context in audit logs. By extracting behaviors as connected system operations, Watson then combines event semantics as the representation of behaviors. To reduce analysis workload, Watson further clusters semantically similar behaviors and distinguishes the representatives for analyst investigation. In our evaluation against both benign and malicious behaviors, Watson exhibits high accuracy for behavior abstraction. Moreover, Watson can reduce analysis workload by two orders of magnitude for attack investigation.

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Junjie Liang (The Pennsylvania State University), Wenbo Guo (The Pennsylvania State University), Tongbo Luo (Robinhood), Vasant Honavar (The Pennsylvania State University), Gang Wang (University of Illinois at Urbana-Champaign), Xinyu Xing (The Pennsylvania State University)

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Jens Müller (Ruhr University Bochum), Dominik Noss (Ruhr University Bochum), Christian Mainka (Ruhr University Bochum), Vladislav Mladenov (Ruhr University Bochum), Jörg Schwenk (Ruhr University Bochum)

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Changming Liu (Northeastern University), Yaohui Chen (Facebook Inc.), Long Lu (Northeastern University)

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Beliz Kaleli (Boston University), Brian Kondracki (Stony Brook University), Manuel Egele (Boston University), Nick Nikiforakis (Stony Brook University), Gianluca Stringhini (Boston University)

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