An Evaluation of Machine Learning Methods for Classifying Bot Traffic in Software Defined Networks

Van Staden, Joshua; Brown, Dane L

Title: An Evaluation of Machine Learning Methods for Classifying Bot Traffic in Software Defined Networks
Creator: Van Staden, Joshua
Creator: Brown, Dane L
Subject: To be catalogued
Date Issued: 2021
Date: 2021
Type: text
Type: article
Identifier: http://hdl.handle.net/10962/465645
Identifier: vital:76628
Identifier: xlink:href="https://link.springer.com/chapter/10.1007/978-981-19-7874-6_72"
Description: Internet security is an ever-expanding field. Cyber-attacks occur very frequently, and so detecting them is an important aspect of preserving services. Machine learning offers a helpful tool with which to detect cyber attacks. However, it is impossible to deploy a machine-learning algorithm to detect attacks in a non-centralized network. Software Defined Networks (SDNs) offer a centralized view of a network, allowing machine learning algorithms to detect malicious activity within a network. The InSDN dataset is a recently-released dataset that contains a set of sniffed packets within a virtual SDN. These sniffed packets correspond to various attacks, including DDoS attacks, Probing and Password-Guessing, among others. This study aims to evaluate various machine learning models against this new dataset. Specifically, we aim to evaluate their classification ability and runtimes when trained on fewer features. The machine learning models tested include a Neural Network, Support Vector Machine, Random Forest, Multilayer Perceptron, Logistic Regression, and K-Nearest Neighbours. Cluster-based algorithms such as the K-Nearest Neighbour and Random Forest proved to be the best performers. Linear-based algorithms such as the Multilayer Perceptron performed the worst. This suggests a good level of clustering in the top few features with little space for linear separability. The reduction of features significantly reduced training time, particularly in the better-performing models.
Format: computer
Format: online resource
Format: application/pdf
Format: 1 online resource (13 pages)
Format: pdf
Publisher: SpringerLink
Language: English
Relation: Proceedings of Third International Conference on Sustainable Expert Systems: ICSES
Relation: van Staden, J. and Brown, D., 2023, February. An Evaluation of Machine Learning Methods for Classifying Bot Traffic in Software Defined Networks. In Proceedings of Third International Conference on Sustainable Expert Systems: ICSES 2022 (pp. 979-991). Singapore: Springer Nature Singapore
Relation: Proceedings of Third International Conference on Sustainable Expert Systems: ICSES p. 979 2021 2367-3389
Rights: Publisher
Rights: Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
Rights: Closed Access

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Van Staden, Joshua (Mr)

Brown, Dane A (Dr)

RU Department of Computer Science

SDG 09. Industry, Innovation and Infrastructure

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