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

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

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728195056
DOI
出版ステータスPublished - 2021
イベント2021 IEEE Wireless Communications and Networking Conference, WCNC 2021 - Nanjing, China
継続期間: 2021 3月 292021 4月 1

出版物シリーズ

名前IEEE Wireless Communications and Networking Conference, WCNC
2021-March
ISSN(印刷版)1525-3511

Conference

Conference2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
国/地域China
CityNanjing
Period21/3/2921/4/1

ASJC Scopus subject areas

  • 工学(全般)

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