A Dual Active Domain Adaptation Approach With Loss Prediction for IIoT Intrusion Detection Under Imperfect Samples

The introduction of wireless terminals has disrupted the previously enclosed landscape of Internet of Things (IIoT), resulting in an expanded cyber-attack surface. Therefore, it is crucial to investigate intrusion detection in the IIoT. Current models rely on big data for training, but imperfect labeled data hampers robust intrusion detection. However, traditional models cannot achieve robust IIoT intrusion detection in the face of imperfect data constraints. Addressing this, we propose IIoT intrusion detection approach under imperfect samples using a hierarchical-split with knowledge distillation neural network (HS-KDNet) and dual active domain adaptation fusion loss prediction (DADA-LP). First, we construct a lightweight feature extraction model (HS-KDNet). HS-KDNet leverages soft labels from knowledge distillation to characterize the similarity between different categories. Next, we develop a single active domain adaptation algorithm through active learning evaluation, which can be used to select a sample of target domains with an active learning value evaluation. Finally, we enhance it into a DADA-LP algorithm, incorporating a loss prediction strategy in the source domain. Moreover, this model ensures outstanding IIoT intrusion detection under imperfect samples, effectively addressing the negative transfer issue. Four datasets from the IIoT are employed to validate the performance of our model. The results unequivocally demonstrate the excellent detection performance when applied to IIoT intrusion detection scenarios under imperfect samples.