Xiaokui Shu           

Xiaokui Shu is a Research Staff Member at the IBM T. J. Watson Research Center and a member of the ACM Future of Computing Academy. Starting from designing penetration tests and creating risk assessments in college, Dr. Shu has been researching in cybersecurity to identify, formalize, and develop creative solutions to cutting-edge security problems. Communications of the ACM featured his anomaly detection approach in 2016; the IEEE Signal Processing Society identified his data leak detection work among the 25 most downloaded papers in 2018; and ACM highlighted his vision on composable graph-based cyber reasoning in the ACM press release. At IBM Research, Dr. Shu leads the cyber reasoning initiative and work with researchers, faculty members, and engineers to advance cyber defense. Before joining IBM, Dr. Shu received his Ph.D. degree in Computer Science at Virginia Tech with Outstanding Ph.D. Student Award.

Research interests: cyber reasoning, knowledge discovery, explainable AI, programming language, anomaly detection, data leak detection, user behavior analytics, program analysis.

News:

• Talk and demo at RSA Conference 2021: The Game of Cyber Threat Hunting: The Return of the Fun
• Research blog published: The thrill of cyber threat hunting with Kestrel Threat Hunting Language
• Open source project released: Kestrel Threat Hunting Language
• Interview with ACM about the future of cyber defense: The pursuit of speed in cybersecurity
• Sponored talk at ACSAC '20: Unleashing Cyber Reasoning: DARPA Transparent Computing Threat Hunting Retrospective
• Paper published at IEEE Big Data 2020: Towards an Open Format for Scalable System Telemetry
• Paper published at DSN 2020: Scarecrow: Deactivating Evasive Malware via Its Own Evasive Logic
• Research blog published: Unleashing Cyber Reasoning Potential in The Era of AI Security
• Paper published at ACM CCS 2018: Threat Intelligence Computing
• Research blog published: Threat Intelligence Computing for Efficient Cyber Threat Hunting
• Book published: Anomaly Detection as a Service: Challenges, Advances, and Opportunities