PSO and Random Forest Techniques to Improve IDS Performance for Multi-class classification

Resumen

With the increasing digitization of the world, the risk of attacks also increases, creating a need to develop effective network intrusion detection techniques. In this research, a two-phase approach was proposed by the authors to improve IDS performance in multi-class classification case. In the first phase, only the relevant features are identified and conserved using an evaluator based on Particle Swarm Optimization. In the second phase, network attacks are classified using the Random Forest classifier. Furthermore, a comparative study is conducted, involving other classifiers such as Naïve Bayes, Stochastic Gradient Descent, Deep Learning, etc. For multi-class classification, the NSL-KDD dataset was used to conduct experiments, and the obtained results showed an accuracy of 99.40%. The performance results of our technique are presented and compared with other competing techniques. The obtained results clearly indicate that our technique outperforms the others.