Bilateral Cognitive Security Games in Networked Control Systems under Stealthy Injection Attacks

2025/05/02 by Anh Tung Nguyen, Quanyan Zhu, Nguyen, Anh Tung +3 · 1 citation
Business, Management and Accounting · Computer Science · Engineering · #Advanced Research in Systems and Signal Processing #Cybersecurity and Information Systems #Economic and Technological Systems Analysis #FOS: Electrical engineering #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2505.01232

openalex publication_date 2025/05/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper studies a strategic security problem in networked control systems under stealthy false data injection attacks. The security problem is modeled as a bilateral cognitive security game between a defender and an adversary, each possessing cognitive reasoning abilities. The adversary with an adversarial cognitive ability strategically attacks some interconnections of the system with the aim of disrupting the network performance while remaining stealthy to the defender. Meanwhile, the defender with a defense cognitive ability strategically monitors some nodes to impose the stealthiness constraint with the purpose of minimizing the worst-case disruption caused by the adversary. Within the proposed bilateral cognitive security framework, the preferred cognitive levels of the two strategic agents are formulated in terms of two newly proposed concepts, cognitive mismatch and cognitive resonance. Moreover, we propose a method to compute the policies for the defender and the adversary with arbitrary cognitive abilities. A sufficient condition is established under which an increase in cognitive levels does not alter the policies for the defender and the adversary, ensuring convergence. The obtained results are validated through numerical simulations.

Citations

• Switching multiplicative watermark design against covert attacks
• Human-AI Collaboration in Cloud Security: Cognitive Hierarchy-Driven Deep Reinforcement Learning
• Scalable and Optimal Security Allocation in Networks against Stealthy Injection Attacks
• Strategic Monitoring of Networked Systems with Heterogeneous Security Levels
• A Bayesian Nash equilibrium-based moving target defense against stealthy sensor attacks
• A Robust Stackelberg Game for Cyber-Security Investment in Networked Control Systems
• Behavioral and Game-Theoretic Security Investments in Interdependent\n Systems Modeled by Attack Graphs

Cited by

• Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations

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