Title

A comparative study of ML-ELM and DNN for intrusion detection

Document Type

Conference Proceeding

Publication Title

ACSW '21: 2021 Australasian Computer Science Week Multiconference

Publisher

Association for Computing Machinery

School

School of Science / ECU Security Research Institute

RAS ID

32899

Funders

Cyber Security Research Centre Limited Australian Government Cooperative Research Centres (CRC) Program

Comments

Yang, W., Wang, S., & Johnstone, M. (2021, February). A comparative study of ML-ELM and DNN for intrusion detection [Paper presentation]. ACSW '21: 2021 Australasian Computer Science Week Multiconference, Dunedin, New Zealand. https://doi.org/10.1145/3437378.3437390

Abstract

© 2021 ACM. 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.

DOI

10.1145/3437378.3437390

Access Rights

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Research Themes

Securing Digital Futures

Priority Areas

Critical Infrastructure

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