Author(s): Ioannis Papadopoulos and Eleni Georgiou

Abstract: The increasing vulnerability of networked control systems (NCSs) to cyber-physical attacks necessitates the development of adaptive security strategies to mitigate risks and ensure system stability. This study explores the Markovian switching-based security strategies for interconnected NCSs, incorporating stochastic optimization and linear matrix inequality (LMI)-based approaches to enhance system resilience against adversarial threats. The primary objectives include formulating a robust mathematical framework that integrates Markovian jump linear systems (MJLSs) with dynamic security mechanisms, designing LMI-based security constraints for stability assurance, and validating the proposed approach through numerical simulations. The study models various cyber-attacks, including denial-of-service (DoS), replay attacks, and hybrid attacks, while employing Markovian switching dynamics to represent system transitions under attack conditions. The results reveal that persistent and hybrid attacks cause significant degradation in system performance, lowering the stability index and control efficiency while increasing response time. Statistical analysis using ANOVA confirms the significant impact of attack types on system stability and efficiency. The findings support the hypothesis that adaptive Markovian switching strategies improve system resilience compared to conventional static security methods. This research contributes to the development of real-time security frameworks for cyber-physical systems, offering practical recommendations for integrating AI-driven detection algorithms, cryptographic security, and hierarchical control strategies to counter evolving cyber threats in industrial automation and smart infrastructures.

DOI: 10.33545/27075923.2025.v6.i1a.84

Pages: 11-16 | Views: 497 | Downloads: 155

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