TY - GEN
T1 - Online algorithms for adaptive cyber defense on Bayesian attack graphs
AU - Hu, Zhisheng
AU - Zhu, Minghui
AU - Liu, Peng
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/10/30
Y1 - 2017/10/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85043363103&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85043363103&partnerID=8YFLogxK
U2 - 10.1145/3140549.3140556
DO - 10.1145/3140549.3140556
M3 - Conference contribution
AN - SCOPUS:85043363103
T3 - MTD 2017 - Proceedings of the 2017 Workshop on Moving Target Defense, co-located with CCS 2017
SP - 99
EP - 109
BT - MTD 2017 - Proceedings of the 2017 Workshop on Moving Target Defense, co-located with CCS 2017
PB - Association for Computing Machinery, Inc
T2 - 4th ACM Workshop on Moving Target Defense, MTD 2017
Y2 - 30 October 2017
ER -