Provenance-based Intrusion Detection: Opportunities and Challenges
Jul 11, 2018
Abstract
Intrusion detection is an arms race; attackers evade intrusion detection systems by developing new attack vectors to sidestep known defense mechanisms. Provenance provides a detailed, structured history of the interactions of digital objects within a system. It is ideal for intrusion detection, because it offers a holistic, attack-vector-agnostic view of system execution. As such, provenance graph analysis fundamentally strengthens detection robustness. We discuss the opportunities and challenges associated with provenance-based intrusion detection and provide insights based on our experience building such systems.
Location
London, UK
Authors
Associate Professor
I am an Associate Professor at the University of Oxford, where I am a member of the Security and Systems research themes.
Previously, I was an Assistant Professor in the Department of Computer Science at the University of British Columbia and remain affiliated with the UBC Security & Privacy Group and the Systopia Lab.
My research asks a fundamental question: How do we make complex systems observable enough to secure, understand, and safely modify them? I explore this question through work on system auditing and accountability, intrusion detection, and mechanisms for safely extending and optimizing complex systems.