research-article

A Comparative Study of ML-ELM and DNN for Intrusion Detection

Authors:

Michael JohnstoneAuthors Info & Claims

ACSW '21: Proceedings of the 2021 Australasian Computer Science Week Multiconference

Article No.: 1, Pages 1 - 7

https://doi.org/10.1145/3437378.3437390

Published: 01 February 2021 Publication History

Abstract

Intrusion detection remains one of the critical research issues in network security. Many machine learning algorithms have been proposed to develop intrusion detection systems, which can categorize network traffic into normal and anomalous classes. The multilayer extreme learning machine (ML-ELM) and the deep neural network (DNN) are two machine learning algorithms based on different theories/concepts that use the same multilayer architecture. In this paper, a comparative study is performed to shed light on the selection between these two algorithms with the same architecture in intrusion detection applications. The study explores the performance of the ML-ELM and DNN algorithms under similar parameter settings. With in-depth analysis and discussions, the limitations and advantages of each algorithm are outlined. In addition, the performance trend of each algorithm with increasing parameter values is studied.

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Cited By

Abu Al-Haija QAltamimi SAlWadi M(2024)Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A surveyExpert Systems with Applications10.1016/j.eswa.2024.124317253(124317)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.124317
Yang WJohnstone MWang SKarie NSahri NKang J(2022)Network Forensics in the Era of Artificial IntelligenceExplainable Artificial Intelligence for Cyber Security10.1007/978-3-030-96630-0_8(171-190)Online publication date: 19-Apr-2022
https://doi.org/10.1007/978-3-030-96630-0_8

Index Terms

A Comparative Study of ML-ELM and DNN for Intrusion Detection

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Published In

cover image ACM Other conferences

ACSW '21: Proceedings of the 2021 Australasian Computer Science Week Multiconference

February 2021

211 pages

ISBN:9781450389563

DOI:10.1145/3437378

Editors:
Nigel Stanger
University of Otago
,
Brendon Woodford
University of Otago
,
Michael Winikoff
Victoria University of Wellington
,
David Eyers
University of Otago
,
Veronica Liesaputra Joachim
University of Otago
,
Daniel Alencar da Costa
University of Otago
,
Andrew Trotman
University of Otago
,
Nigel Stanger
University of Otago
,
Veronica Liesaputra Joachim
University of Otago

Copyright © 2021 ACM.

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Publication History

Published: 01 February 2021

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Cyber Security Research Centre Limited

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ACSW '21

ACSW '21: 2021 Australasian Computer Science Week Multiconference

February 1 - 5, 2021

Dunedin, New Zealand

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Cited By

Abu Al-Haija QAltamimi SAlWadi M(2024)Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A surveyExpert Systems with Applications10.1016/j.eswa.2024.124317253(124317)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.124317
Yang WJohnstone MWang SKarie NSahri NKang J(2022)Network Forensics in the Era of Artificial IntelligenceExplainable Artificial Intelligence for Cyber Security10.1007/978-3-030-96630-0_8(171-190)Online publication date: 19-Apr-2022
https://doi.org/10.1007/978-3-030-96630-0_8

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