Abstract
The problem of limited defense resources owned by the network platform needs to be solved by designing a reasonable defense resource allocation scheme in Industrial Internet of Things (IIoT). However, most of the previously studied defense resource allocation schemes do not consider the impact of network cheat on the defender’s total expected utility, resulting in the defender’s total expected utility not being optimal. To address this problem, this paper proposes a network defense resource allocation scheme with multi-armed bandits (NDRAS) to maximize the defender’s total expected utility. The scheme first proposes a random generation method of node shell configuration based on network cheat, by considering the impact of network cheat on the defender’s total expected utility, masking information about the real configuration of nodes, to increase the uncertainty of the attacker’s attack on each node and thus reduce the likelihood of the attacker’s success. Subsequently, the decomposability and Lipschitz continuity of the defender’s total expected utility is exploited to reduce the gap between the cumulative discrete optimal benefit and the continuous optimal benefit, to maximize the defender’s total expected utility and thus make the defender’s total expected utility optimal. Finally, the detailed experimental results confirm the effectiveness of NDRAS, indicating that the new scheme can give a reasonable defense resource allocation scheme to maximize the defender’s total expected utility.
Supported by the National Natural Science Foundation of China (NSFC) under Grant No. 62172377 and 61872205, and the Natural Science Foundation of Shandong Province under Grant No. ZR2019MF018.
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Acknowledgements
This research is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 62172377 and 61872205, and the Natural Science Foundation of Shandong Province under Grant No. ZR2019MF018.
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Huang, N., Feng, Xc., Zhang, R., Yang, Xg., Xia, H. (2022). Network Defense Resource Allocation Scheme with Multi-armed Bandits. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_27
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DOI: https://doi.org/10.1007/978-3-031-19208-1_27
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