A Hybrid Feature Selection Approach based on Random Forest and Particle Swarm Optimization for IoT Network Traffic Analysis

Abstract

The complexity and volume of network traffic has increased significantly due to the emergence of the “Internet of Things” (IoT). The classification accuracy of the network traffic is dependent on the most pertinent features. In this paper, we present a hybrid feature selection method that takes into account the optimization of Particle Swarms (PSO) and Random Forests. The data collected by the security firm, CIC-IDS2017, contains a large number of attacks and traffic instances. To improve the classification accuracy, we use the framework's RF algorithm to identify the most important features. Then, the PSO algorithm is used to refine the selection process. According to our experiments, the proposed method performed better than the other methods when it comes to the classification accuracy. It achieves a ~99.9% accuracy when using a hybrid of Random Forest and PSO. The hybrid approach also helps improve the model's performance. The suggested method can be utilized by security analysts and network administrators to identify and prevent attacks on the IoT.