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Applying the Bayesian Stackelberg Active Deception Game for Securing Infrastructure Networks

College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
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Entropy 2019, 21(9), 909; https://doi.org/10.3390/e21090909
Received: 7 August 2019 / Revised: 16 September 2019 / Accepted: 16 September 2019 / Published: 18 September 2019
(This article belongs to the Section Complexity)
With new security threats cropping up every day, finding a real-time and smart protection strategy for critical infrastructure has become a big challenge. Game theory is suitable for solving this problem, for it provides a theoretical framework for analyzing the intelligent decisions from both attackers and defenders. However, existing methods are only based on complete information and only consider a single type of attacker, which is not always available in realistic situations. Furthermore, although infrastructure interconnection has been greatly improved, there is a lack of methods considering network characteristics. To overcome these limitations, we focus on the problem of infrastructure network protection under asymmetry information. We present a novel method to measure the performance of infrastructure from the network perspective. Moreover, we propose a false network construction method to simulate how the defender applies asymmetric information to defend against the attacker actively. Meanwhile, we consider multiple types of attackers and introduce the Bayesian Stackelberg game to build the model. Experiments in real infrastructure networks reveal that our approach can improve infrastructure protection performance. Our method gives a brand new way to approach the problem of infrastructure security defense. View Full-Text
Keywords: infrastructure network; Bayesian Stackelberg game; asymmetry information infrastructure network; Bayesian Stackelberg game; asymmetry information
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Zeng, C.; Ren, B.; Liu, H.; Chen, J. Applying the Bayesian Stackelberg Active Deception Game for Securing Infrastructure Networks. Entropy 2019, 21, 909.

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