Abstract: With the proliferation of IoT devices,intrusion detection systems(IDS) are essential to safeguard IoT networks from malicious attacks.However,the scarcity of IoT-specific data limits the effectiveness of traditional methods,while existing domain adaptation approaches often rely on coarse alignment,overlooking intrinsic semantic properties and lowering feature discriminabi-lity.To address these issues,this paper proposes a semi-supervised domain adaptation model,named TDAIID.This model aligns NI domain and II domain at domain,class,and sample levels.The cross-attention mechanism ensures fine-grained feature alignment by focusing on similarities between same-class samples in the source and target domains.Multiple geometric semantic alignment is semantically aligned from both domain-level and class-level perspectives,facilitating the cross-attention mechanism in learning richer and more accurate knowledge from the source NI domain.To fully exploit unlabeled target data,a dynamic center-aware pseudo-labeling algorithm is proposed to improve pseudo-label accuracy and mitigate negative transfer caused by mislabe-ling.Experiments on several widely-used intrusion detection datasets demonstrate that the TDAIID model outperforms state-of-the-art baseline methods,showcasing its superior performance on IoT intrusion detection.

Key words: Domain adaptation, Internet of Things, Intrusion detection, Cross-attention, Transfer learning