Most Vulnerable Attack Trace in a Probabilistic Attack Graph

Abstract

In this talk, we investigate the properties and computation of attack traces on probabilistic attack graphs, a model that integrates probability into traditional attack graphs to reflect the varying exploitability of network vulnerabilities. We introduce the Most Vulnerable Attack Trace (MVAT) problem, which aims to identify the attack trace with the highest cumulative success probability for an attacker.

Xuanli Lin

PhD in Computer Science

My research interests include network optimization, network security, artificial intelligence, and the Internet of Things.