An Efficient Intrusion Detection Method Based on Dynamic Autoencoder

IEEE Wireless Communications Letters, vol. 10, pp. 1707–1711

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.

Authors 8

1. Ruijie Zhao

   Shanghai Jiao Tong University

   Affiliation as printed

   School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai, China

2. Jie Yin

   Nanjing University · Jiangsu Police Officer College

   Affiliation as printed

   Jiangsu Police Institute, Nanjing, China

   State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

3. Zhi Xue

   Shanghai Jiao Tong University

   Affiliation as printed

   School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai, China

4. Guan Gui

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China

5. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, U.K

6. Tomoaki Otsuki Ohtsuki

   Keio University

   Affiliation as printed

   Keio University, Yokohama, Japan

7. Haris Gacanin Aachen Faculty of Electrical Engineering and Information Technology

   RWTH Aachen University

   Affiliation as printed

   Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany

8. Hikmet Sari

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China