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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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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Practical Traffic Analysis Attacks on Secure Messaging Applications

Alireza Bahramali (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst), Ramin Soltani (University of Massachusetts Amherst), Dennis Goeckel (University of Massachusetts Amherst), Don Towsley (University of Massachusetts Amherst)

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Snappy: Fast On-chain Payments with Practical Collaterals

Vasilios Mavroudis (University College London), Karl Wüst (ETH Zurich), Aritra Dhar (ETH Zurich), Kari Kostiainen (ETH Zurich), Srdjan Capkun (ETH Zurich)

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Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning

Harsh Chaudhari (Indian Institute of Science, Bangalore), Rahul Rachuri (Aarhus University, Denmark), Ajith Suresh (Indian Institute of Science, Bangalore)

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