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A multi-objective cost–benefit optimization algorithm for network hardening

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Abstract

Network hardening is an optimization problem to find the best combination of countermeasures to protect a network from cyber-attacks. While an attacker exploits the vulnerabilities in the network, the countermeasures can prevent some exploits by disabling the vulnerabilities. Each countermeasure has different effects; for instance, one avoids data breaches while another protects service availability. As a result, the evaluation criteria for the set of countermeasures are multi-objective. This study proposes a multi-objective network hardening algorithm that enumerates the preferable combinations of security patches near the Pareto front. Our algorithm leverages monotonic security metrics defined naturally on attack graphs. Attack graphs are the graph-structured representation of qualitative dependencies among the security incidents that could occur on a network. There exist several quantitative metrics on attack graphs and have monotonicity. Monotonic security metrics guarantee that increased countermeasures always bring better network security. This property enables a gradient-descent algorithm that searches solutions for combinatorial optimization. We show numerical results which exhibit the remarkable efficiency of our approach. The result implies that the proposed algorithm can be a better alternative to the genetic algorithms used in the past network hardening studies.

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Acknowledgements

The author wishes to acknowledge Dr. Junichi Iijima, Professor in the Department of International Digital and Design Management, School of Management, Tokyo University of Science, and Dr. Keisuke Tanaka, Professor in the Department of Mathematical and Computing Science, School of Computing, Tokyo Institute of Technology, for reviewing and providing constructive advice on the drafts of this article.

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    Kengo Zenitani

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Zenitani, K. A multi-objective cost–benefit optimization algorithm for network hardening. Int. J. Inf. Secur. 21, 813–832 (2022). https://doi.org/10.1007/s10207-022-00586-7

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