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Title: Anomaly detection with various Machine Learning classification techniques over UNSW-NB15 dataset
Author(s): Shushlevska, Martina
Efnusheva, Danijela
Jakimovski, Goran
Todorov, Zdravko
Issue Date: 2022
Language: English
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.
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Appears in Collections:International Conference on Applied Innovations in IT (ICAIIT)

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