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


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 paper, 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
JournalIEEE Wireless Communications Letters
Publication statusAccepted/In press - 2021


  • Artificial neural networks
  • autoencoder
  • Computational modeling
  • deep learning
  • Deep learning
  • Feature extraction
  • intrusion detection
  • lightweight neural network.
  • Performance evaluation
  • Training
  • Wireless sensor networks
  • Wireless sensor networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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