An Efficient Intrusion Detection Method Based on Dynamic Autoencoder

• DOI: 10.1109/lwc.2021.3077946
• Publication Date: 2021-05-06T19:57:10Z
• Authors: Ruijie Zhao; Jie Yin; Zhi Xue; Guan Gui; Bamidele Adebisi; Tomoaki Ohtsuki; Haris Gacanin; Hikmet Sari
• Keywords: Autoencoder; Robustness

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