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.

View More Papers

Broken Metre: Attacking Resource Metering in EVM

Daniel Perez (Imperial College London), Benjamin Livshits (Imperial College London, UCL Centre for Blockchain Technologies, and Brave Software)

Read More

SODA: A Generic Online Detection Framework for Smart Contracts

Ting Chen (University of Electronic Science and Technology of China), Rong Cao (University of Electronic Science and Technology of China), Ting Li (University of Electronic Science and Technology of China), Xiapu Luo (The Hong Kong Polytechnic University), Guofei Gu (Texas A&M University), Yufei Zhang (University of Electronic Science and Technology of China), Zhou Liao (University…

Read More

Complex Security Policy? A Longitudinal Analysis of Deployed Content...

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)

Read More

Precisely Characterizing Security Impact in a Flood of Patches...

Qiushi Wu (University of Minnesota), Yang He (University of Minnesota), Stephen McCamant (University of Minnesota), Kangjie Lu (University of Minnesota)

Read More