TY - GEN
T1 - A Large-Scale Markov Game Approach to Dynamic Protection of Interdependent Infrastructure Networks
AU - Huang, Linan
AU - Chen, Juntao
AU - Zhu, Quanyan
N1 - Funding Information:
This research is partially supported by NSF grants EFRI-1441140, SES-1541164, CNS-1544782, DOE grant DE-NE0008571, and a DHS CIRI grant.
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - The integration of modern information and communication technologies (ICTs) into critical infrastructures (CIs) improves its connectivity and functionalities yet also brings cyber threats. It is thus essential to understand the risk of ICTs on CIs holistically as a cyber-physical system and design efficient security hardening mechanisms. To this end, we capture the system behaviors of the CIs under malicious attacks and the protection strategies by a zero-sum game. We further propose a computationally tractable approximation for large-scale networks which builds on the factored graph that exploits the dependency structure of the nodes of CIs and the approximate dynamic programming tools for stochastic Markov games. This work focuses on a localized information structure and the single-controller game solvable by linear programming. Numerical results illustrate the proper tradeoff of the approximation accuracy and computation complexity in the new design paradigm and show the proactive security at the time of unanticipated attacks.
AB - The integration of modern information and communication technologies (ICTs) into critical infrastructures (CIs) improves its connectivity and functionalities yet also brings cyber threats. It is thus essential to understand the risk of ICTs on CIs holistically as a cyber-physical system and design efficient security hardening mechanisms. To this end, we capture the system behaviors of the CIs under malicious attacks and the protection strategies by a zero-sum game. We further propose a computationally tractable approximation for large-scale networks which builds on the factored graph that exploits the dependency structure of the nodes of CIs and the approximate dynamic programming tools for stochastic Markov games. This work focuses on a localized information structure and the single-controller game solvable by linear programming. Numerical results illustrate the proper tradeoff of the approximation accuracy and computation complexity in the new design paradigm and show the proactive security at the time of unanticipated attacks.
UR - http://www.scopus.com/inward/record.url?scp=85032874493&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032874493&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68711-7_19
DO - 10.1007/978-3-319-68711-7_19
M3 - Conference contribution
AN - SCOPUS:85032874493
SN - 9783319687100
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 357
EP - 376
BT - Decision and Game Theory for Security - 8th International Conference, GameSec 2017, Proceedings
A2 - Kiekintveld, Christopher
A2 - Schauer, Stefan
A2 - An, Bo
A2 - Rass, Stefan
A2 - Fang, Fei
PB - Springer Verlag
T2 - 8th International Conference on Decision and Game Theory for Security, GameSec 2017
Y2 - 23 October 2017 through 25 October 2017
ER -