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Optimal Innovation-Based Deception Attacks on Multi-Channel Cyber–Physical Systems

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Publicado en:Electronics vol. 14, no. 8 (2025), p. 1569
Autor principal: Yang, Xinhe
Otros Autores: Zhu, Ren, Zhou Jingquan, Huang, Jing
Publicado:
MDPI AG
Materias:
Innovations
Scheduling
Divergence
Communication channels
Semidefinite programming
Cyber-physical systems
Normal distribution
Sensors
Support vector machines
State estimation
Error analysis
Algorithms
Deception
Acceso en línea:Citation/Abstract
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Resumen:This article addresses the optimal scheduling problem for linear deception attacks in multi-channel cyber–physical systems. The scenario where the attacker can only attack part of the channels due to energy constraints is considered. The effectiveness and stealthiness of attacks are quantified using state estimation error and Kullback–Leibler divergence, respectively. Unlike existing strategies relying on zero-mean Gaussian distributions, we propose a generalized attack model with Gaussian distributions characterized by time-varying means. Based on this model, an optimal stealthy attack strategy is designed to maximize remote estimation error while ensuring stealthiness. By analyzing correlations among variables in the objective function, the solution is decomposed into a semi-definite programming problem and a 0–1 programming problem. This approach yields the modified innovation and an attack scheduling matrix. Finally, numerical simulations validate the theoretical results.
ISSN:2079-9292
DOI:10.3390/electronics14081569
Fuente:Advanced Technologies & Aerospace Database