Most Vulnerable Attack Trace in a Probabilistic Attack Graph

Oct 31, 2024· Xuanli Lin · 0 min read
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.
Date
Oct 31, 2024 11:00 AM — 11:20 AM
Event
Location

Washington Hilton (Woodley)

1919 Connecticut Ave NW, Washington, DC 20009

event
Attack Graph Attack Trace
Xuanli Lin
Authors
Xuanli Lin
PhD Student in Computer Science

Xuanli Lin is a fifth-year PhD student in the Computer Science department at Arizona State University, supervised by Dr. Guoliang Xue.

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