Adaptive Defense Optimization Under Intelligent Data Poisoning Attacks in Industrial Control System
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Summary
This paper proposes a detection-aware stochastic optimization framework for industrial control systems facing asynchronous, unreliable feedback and adversarial data poisoning. It uses the SWaT dataset to model poisoning behavior and an adaptive acknowledgment (ACK) bundling mechanism, driven by a multi-objective stochastic gradient descent (SGD) algorithm with a residual-based anomaly indicator, to adjust feedback timing. Numerical experiments on an autonomous surface vehicle (ASV) benchmark show closed-loop recovery under AI-driven data poisoning attacks.
Mitigation
The source describes an adaptive ACK bundling mechanism that regulates feedback timing and adjusts the ACK bundling window online when anomalous behavior is detected, with Lyapunov–Krasovskii (LK)-based conditions established to ensure closed-loop stability. It does not describe a patch, fixed version or configuration fix for a specific product.