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Learning-Based Adaptive Thresholding and Data Encryption–Decryption for Event-Triggered Cyber–Physical Systems Under Strategic DoS Attacks
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Summary
This research paper proposes a resilient control method for event-triggered cyber-physical systems facing strategic denial-of-service attacks, where attackers target packets based on their observable importance. The method combines a multi-objective Q-Learning strategy that adjusts the trigger threshold of the event-triggered mechanisms with a data encryption-decryption scheme using a Logistic map and differential encoding to distort state-dependent statistical features. The authors report that comparative experiments verify the method's effectiveness and superiority.
Mitigation
The source describes its proposed method, namely learning-based adaptive thresholding via multi-objective Q-Learning plus Logistic map and differential encoding data en-decryption, as the countermeasure, but does not state a separate fix, patch, configuration change or workaround.