Continual Learning Backdoors in Industrial IoT and CPS
The integration of Continual Learning (CL) pipelines in IoT and Cyber-Physical Systems (CPS) has introduced a "persistence paradox" where adaptation mechanisms are leveraged to embed permanent backdoors. Attackers exploit replay buffers, latent space representation reuse, and incremental weight manipulation to ensure malicious triggers survive multiple retraining cycles. These vectors specifically target Industrial IoT (IIoT) edge controllers and Smart Grid reinforcement learning agents, allowing dormant triggers to bypass anomaly detection and cause physical-world failures. Because these backdoors are integrated into the evolving learned baseline, traditional remediation strategies—including periodic weight resetting and model retraining—are rendered ineffective.