X-GATE: Attribution-Aware Distillation and Hardening for Compressed Edge-IIoT Intrusion Detection

Industrial Internet of Things (IIoT) intrusion detection requires compact, latency-efficient models whose behavior remains assessable under adversarial stress, yet compression can alter the feature-attribution structure learned by a full-precision model. This paper presents X-GATE (eXplanation-Guided Adversarial Training Engine), an attribution-aware training framework for compressed Edge-IIoT intrusion detection. X-GATE combines Explanation-Consistency Distillation (ECD), which aligns Teacher–Student feature-attribution rankings with a differentiable soft-rank Spearman penalty, and Explanation-Guided Adversarial Training (EGAT), which hardens the Student on Teacher-salient feature coordinates. On the full Edge-IIoTset 2022 benchmark, the latest three-seed ablation gives Full X-GATE 89.30 ± 3.89% F1-Macro with 0.617 M parameters, within approximately 0.6 percentage points of the full-precision Teacher; a Random Forest model remains a stronger clean-F1 reference, so X-GATE is not framed as the clean-accuracy optimum. In a separate deployment-subset rerun, X-GATE obtains 78.83 ± 5.83% float F1-Macro and 79.11 ± 5.47% INT8 F1-Macro, reduces the adversarial false-positive rate from 0.46 ± 0.08% for KD-only to 0.16 ± 0.09% under the evaluated single-step white-box explanation-evasion protocol, and reduces CPU latency from 4.16 to 1.25 ms/sample. Component ablation further shows that ECD reduces Logical Drift by 17.24%, while EGAT improves adversarial F1 by 10.57 percentage points. Taken together, these benchmark- and protocol-bounded results position X-GATE as a compact neural operating point for the Edge-IIoT setting studied here, balancing attribution consistency, targeted hardening, and CPU-side efficiency.