Optimal Fusion Deception Attacks Design and Game Analysis in Multi-Sensor Systems

IF 7.9 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-30 DOI:10.1109/TNSE.2025.3565903

Chuanyi Ning;Fei Hao;Jiping Yang

引用次数: 0

Abstract

In this paper, the problems of designing fusion deception attacks against multi-sensor systems are investigated. Some new fusion deception attack models have been designed. The trace of the estimation error is used to evaluate the attack performance and the Kullback-Leibler (K-L) divergence is utilized to evaluate the stealthiness. The benchmarking is to compare the estimation errors under the same stealthiness constraint. It has been verified that the proposed attack schemes perform better than the existing ones. Besides, the consideration of correlation help to improve the attack performance as well. A game framework was also established to study the interaction between the attacker and the defender. Based on the game analysis, optimal attack scheme and defense policy are simultaneously derived to achieve a Nash equilibrium. Finally, simulation results are provided to illustrate the theoretical results.

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多传感器系统中最优融合欺骗攻击设计与博弈分析

研究了针对多传感器系统的融合欺骗攻击设计问题。设计了几种新的融合欺骗攻击模型。利用估计误差跟踪来评估攻击性能，利用K-L散度来评估隐身性。基准测试是比较在相同隐身性约束下的估计误差。实验证明，所提出的攻击方案的性能优于现有的攻击方案。此外，对相关性的考虑也有助于提高攻击性能。建立了一个博弈框架来研究攻击者和防御者之间的相互作用。在博弈分析的基础上，同时导出了最优攻击方案和防御策略，以达到纳什均衡。最后给出了仿真结果来验证理论结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.