Proceedings of International Conference on Applied Innovation in IT
2022/03/09, Volume 10, Issue 1, pp.21-27

Anomaly Detection with Various Machine Learning Classification Techniques over UNSW-NB15 Dataset

Martina Shushlevska, Danijela Efnusheva, Goran Jakimovski, Zdravko Todorov

Abstract: The exponential growth of computers and devices connected to the Internet and the variety of commercial services offered creates the need to protect Internet users. As a result, intrusion detection systems (IDS) are becoming an essential part of each computer-communication system, detecting and responding to malicious network traffic and computer abuse. In this paper, an IDS based on the UNSW-NB15 dataset has been implemented. The results obtained indicate F1 Score and Recall values of 76.1% and 85.3% for the Naive Bayes algorithm, 78.2% and 96.1% for Logistic Regression algorithm, 88.3% and 95.4% for Decision Tree classifier, and 89.3% and 98.5% for Random Forest.

Keywords: Anomaly Detection, Intrusion Detection System, Machine Learning, Network Security, UNSW-NB15 Dataset

DOI: 10.25673/76928

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