RL-ACID: Reinforcement Learning-Optimized Adaptive Causal Discovery for Robust Anomaly Detection in Industrial Systems

Anomaly detection is essential for the security of industrial control systems. However, dynamic operating conditions introduce nonstationarity and time-varying causal structures, degrading performance and undermining interpretability. To address this, we propose RL-ACID, a lightweight reinforcement learning framework for adaptive causal discovery. It reformulates anomaly detection as sequential causal discovery, introducing the first unified architecture that integrates lightweight reinforcement learning-based search with causal clustering to resolve the adaptability, efficiency, and interpretability tradeoff. Our framework employs a joint time-frequency encoder to extract and construct candidate causal graphs. Building upon this, we design the causal reinforcement learning-based lightweight search algorithm, which formulates graph exploration as a sequential decision process under sparsity and acyclicity constraints, enabling iterative causal structure optimization. To further enhance adaptability in dynamic environments, we introduce a causal clustering module that softly assigns time-varying graphs to latent operational modes through structural experts, thereby distinguishing normal operational fluctuations from true anomalies. Extensive experiments on multiple industrial benchmarks demonstrate the superior performance of RL-ACID. Our framework not only achieves higher accuracy than baselines but also provides interpretable anomaly analysis through causal path tracing.