A Domain Adaptive IoT Intrusion Detection Algorithm Based on GWR-GCN Feature Extraction and Conditional Domain Adversary

In the field of Internet of Things (IoT), the intrusion detection data is scarce because of the network security and privacy. This article proposes a domain adaptive IoT intrusion detection algorithm based on GWR-GCN feature extraction and conditional domain adversary, which aims to improve intrusion detection in the IoT domain by learning from other intrusion detection domains with rich data. First, a GWR-GCN-based domain-invariant feature extraction method is proposed, where the growing when required network (GWR) calculates the correlation between the original data, and the related data is connected into a graph by the Hebb learning principle. The graph convolutional neural network (GCN) is used to mine the feature information of the graph-structured data and extract the optimal domain-invariant features. Second, a Copula-based data distribution alignment method is proposed to decompose the overall feature distribution difference between the source and target domains into the marginal distribution difference of a single feature and the joint distribution difference between features. Meanwhile, the correlation between features on the data distribution is considered to further reduce the data distribution difference and improve the cross-domain ability. Finally, a conditional domain adversarial intrusion detection model is proposed to improve the detection performance by adding the class information as a condition in the discriminator, considering the correlation between features and classes, and reducing the effect of domain shift on distributional alignment. In order to verify the proposed algorithm, experiments are conducted on the traditional network and the IoT domain data sets, and the superiority is verified on multiple evaluation indicators.