工業物聯網網絡中輕量級入侵檢測模型的跨域泛化失敗

摘要

輕量級機器學習模型因其適用於資源受限的邊緣部署，愈來愈多被提出用於工業物聯網（IIoT）網路中的入侵偵測。大多數報告的結果僅在這些模型所訓練的網路中進行評估，而未驗證其在未見過網路上的行為。本研究在一個IIoT資料集上訓練四種輕量級架構，並在不重新訓練的情況下，使用僅限於三個資料來源共有的屬性所構成的特徵表示，對兩個結構不同的IIoT資料集進行評估。針對兩個表現最佳的模型進行可解釋性分析顯示，兩者均壓倒性地依賴粗略的連接埠類別特徵；最具影響力的類別在來源域攻擊流量中的出現率，是目標域中的96至435倍，這表明細化連接埠解析度並未移除已知的捷徑，而是將其重新定位。在自然不平衡的類別分布下進行評估，揭示了進一步的效應：所使用的評估流程可能反轉哪個目標網路看似構成更大的泛化挑戰。同時也評估了對抗性穩健性與透過少量目標域曝露的恢復能力；對抗性擾動的穩健性與跨網路泛化無關，且透過適應的恢復效果因架構而有顯著差異。這些發現表明，應在真實的類別分布下採用跨網路評估，而非僅依賴域內準確率，來評估部署的可行性。

English

Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resource-constrained edge deployment. Most reported results evaluate these models only within their training network, leaving behavior on unseen networks unverified. This study trains four lightweight architectures on one IIoT dataset and evaluates them, without retraining, on two structurally distinct IIoT datasets using a feature representation restricted to attributes available across all three sources. Explainability analysis across two top-performing models shows both rely overwhelmingly on coarse port-category features; the most influential category occurs in source-domain attack traffic at 96 to 435 times the rate in the two target domains, indicating that coarsening port resolution relocates rather than removes a documented shortcut. Evaluation under naturally imbalanced class distributions reveals a further effect: the evaluation protocol used can reverse which target network appears to pose the greater generalization challenge. Adversarial robustness and recovery through limited target-domain exposure are also assessed; robustness to adversarial perturbation is unrelated to cross-network generalization, and recovery through adaptation varies considerably by architecture. These findings suggest deployment readiness should be assessed using cross-network evaluation under realistic class distributions, rather than within-domain accuracy alone.