Hybrid intrusion detection system based on Random forest, decision tree and Multilayer Perceptron (MLP) algorithms

Abstract

With the rapid increase in network intrusions, applying an active network intrusion defense is more important than ever before. Different learning algorithms have been combined to achieve better performance. To improve the accuracy and efficiency of the network intrusion detection system, a new hybrid algorithm is designed, which combines the Random forest, Decision tree and Multilayer Perceptron (MLP) algorithms. The experimental results show that the hyprid model has a better true Positive Rate (TPR) for attack activities, rapidly data preprocessing speed, and shorter training time. In particular, the accuracy of multi-class classification can reach as high as 99.7% in the NSL-KDD dataset, 77.99% in the UNSW-NB15 dataset and 84.89% in the CIC-IDS-2017.