Please use this identifier to cite or link to this item:
http://dx.doi.org/10.25673/76928
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. |
URI: | https://opendata.uni-halle.de//handle/1981185920/78880 http://dx.doi.org/10.25673/76928 |
Open Access: | ![]() |
License: | ![]() |
Appears in Collections: | International Conference on Applied Innovations in IT (ICAIIT) |
Files in This Item:
File | Description | Size | Format | |
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1_4 Shushlevska.pdf | 508.31 kB | Adobe PDF | ![]() View/Open |