Cross-Domain Robust Dynamic Trust Evaluation for Industrial Internet of Things Edge NodesOA

To address trust-score drift and unsafe online adaptation under cross-domain attack-contaminated streams in Industrial Internet of Things(IIoT)edge environments,this paper proposes a risk-aware lightweight test-time adaptation(TTA)framework,named RaL-TTA,for dynamic trust evaluation of edge nodes.RaL-TTA constructs a low-dimensional robust feature space and a source-domain normal-entropy reference baseline,and performs selective online maintenance in the target domain through Kolmogorov–Smirnov(KS)drift detection,SafeBrake risk gating,Adaptive Batch Normalization(AdaBN)anchor protection,and budgeted sample-level safeguards.Low-risk batches are adapted by updating only lightweight Batch Normalization(BN)parameters,whereas high-risk batches freeze online updates and invoke anchor-based protective inference.Experiments on Edge-IIoTset show that RaL-TTA substantially improves perturbation-stage attack detection and false-positive control compared with general TTA baselines while maintaining post-perturbation stability.In the main Edge-IIoTset setting,RaL-TTA achieves a perturbation-stage true positive rate(TPR)of 1.0000,false positive rate(FPR)of 0.0410,F1-score of 0.9544,and accuracy of 0.9713,while updating only 192 online parameters.External validation on X-IIoTID,a connectivity-and device-agnostic intrusion dataset for IIoT,further evaluates cross-service generalization under Modbus,Message Queuing Telemetry Transport(MQTT),and WebSocket target services.Additional sensitivity,startup-window robustness,calibration,and runtime-overhead analyses further characterize the stability,deployment assumptions,trust-score reliability,and edge-side feasibility of the proposed framework.