Comparative Evaluation of Machine Learning Algorithms for Network Intrusion Detection and Attack Classification
Miguel León, Tijana Markovic, Sasikumar Punnekkat
Abstract
With the increasing use of the internet and reliance on computer-based systems for our daily lives, any vulnerability in those systems is one of the most important issues for the community. For this reason, the need for intelligent models that detect malicious intrusions is important to keep our personal information safe. In this paper, we investigate several supervised (Artificial Neural Network, Support Vector Machine, Random Forest, Linear Discriminant Analysis, and K-Nearest Neighbors) and unsupervised (K-means, Mean-shift, and DBSCAN) machine learning algorithms, in the context of anomaly-based Intrusion Detection Systems. We are using four different IDS benchmark datasets (KDD99, NSL-KDD, UNSW-NB15, and CIC-IDS-2017) to evaluate the performance of the selected machine learning algorithms for both intrusion detection and attack classification. The results have shown that Random Forest is the most suitable algorithm regarding model accuracy and execution time.