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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Shivam Bhasin (Nanyang Technological University), Anupam Chattopadhyay (Nanyang Technological University), Annelie Heuser (Univ Rennes, Inria, CNRS, IRISA), Dirmanto Jap (Nanyang Technological University), Stjepan Picek (Delft University of Technology), Ritu Ranjan Shrivastwa (Secure-IC)

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Honggang Yu (University of Florida), Kaichen Yang (University of Florida), Teng Zhang (University of Central Florida), Yun-Yun Tsai (National Tsing Hua University), Tsung-Yi Ho (National Tsing Hua University), Yier Jin (University of Florida)

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Sebastian Roth (CISPA Helmholtz Center for Information Security), Timothy Barron (Stony Brook University), Stefano Calzavara (Università Ca' Foscari Venezia), Nick Nikiforakis (Stony Brook University), Ben Stock (CISPA Helmholtz Center for Information Security)

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