Xueyuan Han (Harvard University), Thomas Pasquier (University of Bristol), Adam Bates (University of Illinois at Urbana-Champaign), James Mickens (Harvard University), Margo Seltzer (University of British Columbia)

Advanced Persistent Threats (APTs) are difficult to detect due to their “low-and-slow” attack patterns and frequent use of zero-day exploits. We present UNICORN, an anomaly-based APT detector that effectively leverages data provenance analysis. From modeling to detection, UNICORN tailors its design specifically for the unique characteristics of APTs. Through extensive yet time-efficient graph analysis, UNICORN explores provenance graphs that provide rich contextual and historical information to identify stealthy anomalous activities without pre-defined attack signatures. Using a graph sketching technique, it summarizes long-running system execution with space efficiency to combat slow-acting attacks that take place over a long time span. UNICORN further improves its detection capability using a novel modeling approach to understand long-term behavior as the system evolves. Our evaluation shows that UNICORN outperforms an existing state-of-the-art APT detection system and detects real-life APT scenarios with high accuracy.

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Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer

Zhongjie Ba (Zhejiang University and McGill University), Tianhang Zheng (University of Toronto), Xinyu Zhang (Zhejiang University), Zhan Qin (Zhejiang University), Baochun Li (University of Toronto), Xue Liu (McGill University), Kui Ren (Zhejiang University)

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Melting Pot of Origins: Compromising the Intermediary Web Services...

Takuya Watanabe (NTT), Eitaro Shioji (NTT), Mitsuaki Akiyama (NTT), Tatsuya Mori (Waseda University, NICT, and RIKEN AIP)

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ABSynthe: Automatic Blackbox Side-channel Synthesis on Commodity Microarchitectures

Ben Gras (Vrije Universiteit Amsterdam, Intel Corporation), Cristiano Giuffrida (Vrije Universiteit Amsterdam), Michael Kurth (Vrije Universiteit Amsterdam), Herbert Bos (Vrije Universiteit Amsterdam), Kaveh Razavi (Vrije Universiteit Amsterdam)

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