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Luu, T.J.

Learning-Based Adaptive Thresholding and Data Encryption–Decryption for Event-Triggered Cyber–Physical Systems Under Strategic DoS Attacks

inforesearchPeer-Reviewed

researchsecurity

Source: IEEE Xplore (Security & AI Journals)July 9, 2026

Summary

This research addresses vulnerabilities in cyber-physical systems (CPSs, which are physical machines controlled and monitored by computers) that use event-triggered mechanisms (ETMs, systems that send data only when something important happens rather than continuously). The paper proposes a defense method combining machine learning-based adaptive thresholding (automatically adjusting sensitivity levels using AI) and encryption to protect against strategic DoS attacks (targeted jamming where attackers selectively block critical data packets based on what they learn about the system).

Solution / Mitigation

The paper proposes three technical defenses: (1) a multi-objective Q-Learning strategy (a machine learning approach that dynamically adjusts when the system sends data to balance performance, communication efficiency, and security), (2) a data encryption-decryption scheme combining Logistic map with differential encoding to distort the statistical features of data so attackers cannot identify which packets are important, and (3) an online parameter optimization algorithm designed to work within strict energy constraints.