Cyber-physical attack detection in industrial robotic arms via reinforcement learning with systemic heterogeneous data fusion

Industrial robotic arms in modern industry face rising cyber-physical attacks due to increased interconnectivity, demanding robust detection mechanisms. Common unsupervised methods struggle with instability from fixed threshold settings, and simple fusion of heterogeneous sensor data often yields poor results due to signal inconsistencies. This paper proposes an integration-oriented reinforcement learning (RL)-based framework combining heterogeneous data fusion, domain adaptation, and pseudo-label-guided training. Specifically, domain adaptation maps diverse industrial sensor signals into a unified latent space, extracting key features that mitigate signal inconsistencies and enable effective data fusion. These features, combined with pseudo-labels from an unsupervised anomaly detection module, constitute the state space for an RL agent. Leveraging this integrated state, the agent learns an adaptive policy-based decision rule over fused representations. To tackle reward sparsity, an Intrinsic Curiosity Module is incorporated into the RL framework, providing intrinsic rewards that encourage exploration of rare anomalies. This design addresses two major challenges: mitigating inconsistencies in heterogeneous signals (i.e. from different types of sensors) and reducing reliance on hand-tuned fixed thresholds. Experimental results show that the proposed approach improves PR-AUC by a margin ranging from 3 to 27 percentage points compared with representative baselines under the same evaluation protocol, indicating improved detection performance on the evaluated robotic anomaly-detection benchmarks. © 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. This article is available under the terms of the IOP-Standard License.