An Efficient Intrusion Detection Method Based on Dynamic Autoencoder

Ruijie Zhao, Jie Yin, Zhi Xue, Guan Gui, Bamidele Adebisi, Tomoaki Ohtsuki, Haris Gacanin, Hikmet Sari

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The proliferation of wireless sensor networks (WSNs) and their applications has attracted remarkable growth in unsolicited intrusions and security threats, which disrupt the normal operations of the WSNs. Deep learning (DL)-based network intrusion detection (NID) methods have been widely investigated and developed. However, the high computational complexity of DL seriously hinders the actual deployment of the DL-based model, particularly in the devices of WSNs that do not have powerful processing performance due to power limitation. In this letter, we propose a lightweight dynamic autoencoder network (LDAN) method for NID, which realizes efficient feature extraction through lightweight structure design. Experimental results show that our proposed model achieves high accuracy and robustness while greatly reducing computational cost and model size.

Original languageEnglish
Article number9424716
Pages (from-to)1707-1711
Number of pages5
JournalIEEE Wireless Communications Letters
Volume10
Issue number8
DOIs
Publication statusPublished - 2021 Aug

Keywords

  • Wireless sensor networks
  • autoencoder
  • deep learning
  • intrusion detection
  • lightweight neural network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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