A novel approach based on lightweight deep neural network for network intrusion detection

Ruijie Zhao, Zhaojie Li, Zhi Xue, Tomoaki Ohtsuki, Guan Gui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

With the ubiquitous network applications and the continuous development of network attack technology, all social circles have paid close attention to the cyberspace security. Intrusion detection systems (IDS) plays a very important role in ensuring computer and communication systems security. Recently, deep learning has achieved a great success in the field of intrusion detection. However, the high computational complexity poses a major hurdle for the practical deployment of DL-based models. In this paper, we propose a novel approach based on a lightweight deep neural network (LNN) for IDS. We design a lightweight unit that can fully extract data features while reducing the computational burden by expanding and compressing feature maps. In addition, we use inverse residual structure and channel shuffle operation to achieve more effective training. Experiment results show that our proposed model for intrusion detection not only reduces the computational cost by 61.99% and the model size by 58.84%, but also achieves satisfactory accuracy and detection rate.

Original languageEnglish
Title of host publication2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728195056
DOIs
Publication statusPublished - 2021
Event2021 IEEE Wireless Communications and Networking Conference, WCNC 2021 - Nanjing, China
Duration: 2021 Mar 292021 Apr 1

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2021-March
ISSN (Print)1525-3511

Conference

Conference2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
Country/TerritoryChina
CityNanjing
Period21/3/2921/4/1

Keywords

  • Deep learning
  • Intrusion detection
  • Lightweight neural network
  • Network applications

ASJC Scopus subject areas

  • Engineering(all)

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