Intrusion Detection Method of Internet of Things Based on Multi GBDT Feature Dimensionality Reduction and Hierarchical Traffic Detection

Abstract

The rapid development of Internet of Things (IoT) technology has brought great convenience to people's life. However, the security protection capability of IoT is weak and vulnerable. Therefore, more protection needs to be done for the security of IoT. The paper proposes an intrusion detection method for IoT based on multi GBDT feature reduction and hierarchical traffic detection model. Firstly, GBDT is used to filter the features of IoT traffic data sets BoT-IoT and UNSW-NB15 to reduce the traffic feature dimension. At the same time, in order to improve the reliability of feature filtering, this paper constructs multiple GBDT models to filter the features of multiple sub data sets, and comprehensively evaluates the filtered features to find out the best alternative features. Then, two neural networks are trained with the two data sets after dimensionality reduction, and the traffic will be detected with the trained neural network. In order to improve the efficiency of traffic detection, this paper proposes a hierarchical traffic detection model, which can reduce the computational cost and time cost of detection process. Experiments show that the multi GBDT dimensionality reduction method can obtain better features than the traditional PCA dimensionality reduction method. Besides, the use of dual data sets improves the comprehensiveness of the IoT intrusion detection system, which can detect more types of attacks, and the hierarchical traffic model improves the detection efficiency of the system.