2023/02/28 by Vusumuzi Malele, Malele, Vusumuzi, Topside E. Mathonsi +1 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Computer Science and Game Theory (cs.GT) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Network Security and Intrusion Detection
paper · pdf · doi:10.48550/arxiv.2302.14374
openalex publication_date 2023/02/28 · openalex created_date 2023/03/03 · openalex updated_date 2026/07/28
Machine learning, statistical-based, and knowledge-based methods are often used to implement an Anomaly-based Intrusion Detection System which is software that helps in detecting malicious and undesired activities in the network primarily through the Internet. Machine learning comprises Supervised, Semi-Supervised, and Unsupervised Learning algorithms. Supervised machine learning uses a trained label dataset. This paper uses four supervised learning algorithms Random Forest, XGBoost, K-Nearest Neighbours, and Artificial Neural Network to test the performance of the public dataset. Based on the prediction accuracy rate, the results show that Random Forest performs better on multi-class Intrusion Detection System, followed by XGBoost, K-Nearest Neighbours respective, provided prediction accuracy is taken into perspective. Otherwise, K-Nearest Neighbours was the best performer considering the time of training as the metric. It concludes that Random Forest is the best-supervised machine learning for Intrusion Detection System