Random Feature Graph Neural Network for Intrusion Detection in Internet of Things

At present, intrusion detection mainly relies on traditional deep learning methods, but this method ignores the association between data records. Although the graph neural network method considers the relationship between the stream data records, it ignores the feature relationship between the graph nodes. Therefore, a random feature graph neural network iot intrusion detection model is proposed to solve these problems. The graph structure of network communication dataset is constructed. Random features are introduced to enrich the features of graph nodes, so as to improve the expression ability of graph neural network. An intrusion detection classifier is constructed to accurately detect attack traffic by training graph neural network with the extracted traffic interrelation. The experimental results show that compared with several classical machine learning and deep learning algorithms and the latest graph neural network detection algorithms, the accuracy of the proposed method can be improved by 17.90 and 1.43 percentage points. In terms of multi-classification detection, F1 scores are higher than other algorithms on most attack categories. In addition, the number of layers K of the graph neural network and the optimal selection of the aggregator are determined by experiments. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.