Paper Info
Title
Online Algorithms for Adaptive Cyber Defense on Bayesian Attack Graphs.
Abstract
Emerging zero-day vulnerabilities in information and communications technology systems make cyber defenses very challenging. In particular, the defender faces uncertainties of; e.g., system states and the locations and the impacts of vulnerabilities. In this paper, we study the defense problem on a computer network that is modeled as a partially observable Markov decision process on a Bayesian attack graph. We propose online algorithms which allow the defender to identify effective defense policies when utility functions are unknown a priori. The algorithm performance is verified via numerical simulations based on real-world attacks.
Year
Venue
DocType
2017
MTD@CCS
Conference
ISBN
Citations 
PageRank 
978-1-4503-5176-8
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Zhisheng Hu173.86
Minghui Zhu24412.11
Peng Liu31701171.49