Comparative Algorithm Analysis for Machine Learning Based Intrusion Detection System
Sharuka Promodya Thirimanne, Lasitha Jayawardana, Pushpika Liyanaarachchi, S.L.P. Yasakethu · 2021
In recent years, various types of new intrusions that differ from existing ones have been identified. Moreover, due to the rapid evolution of cyberattacks, machine learning algorithms require updated datasets that comprise the most recent intrusions. The prime objective of this research is to discover the best machine learning algorithm for intrusion detection trained using the NSL-KDD and the UNSW-NB15 datasets and perform a comparative analysis between six machine learning algorithms classified as supervised, semi-supervised, and unsupervised learning. This study revealed that the performance of supervised and semi-supervised machine learning algorithms outperformed unsupervised machine learning algorithms for both datasets and concluded that Support Vector Machines (SVM) and Deep Neural Network (DNN) perform better for NSL-KDD and UNSW-NB15, respectively.