Netinfo Security ›› 2023, Vol. 23 ›› Issue (1): 66-72.doi: 10.3969/j.issn.1671-1122.2023.01.008

Previous Articles     Next Articles

Lightweight IoT Intrusion Detection Method Based on Feature Selection

LIU Xiangyu, LU Tianliang, DU Yanhui(), WANG Jingxiang   

  1. School of Information Network Security, People’s Public Security University of China, Beijing 100038, China
  • Received:2022-11-21 Online:2023-01-10 Published:2023-01-19
  • Contact: DU Yanhui E-mail:duyanhui@ppsuc.edu.cn

Abstract:

With the large-scale use of the Internet of Things (IoT), the security problem has become increasingly prominent. How to detect network attacks accurately and in real time in the IoT environment with limited resources is a key problem that needs to be solved urgently. Intrusion detection system based on network traffic features is a solution to the security of IoT. This solution remains the problem of the large number of features make training fast and lightweight detection models difficult. To address this issue, this paper proposed a feature selection technique based on Pearson correlation coefficient and variance expansion factor. In this method, traffic characteristics were selected under flow granularity, and normal and malicious traffic were classified by machine learning algorithm. The experimental results show that this method can quickly and effectively detect network attacks with limited resources, and the overall precision and recall reach 99.4%.

Key words: Internet of Things, intrusion detection, machine learning, feature selection

CLC Number: