A Framework of Intrusion Detection System based on Bayesian Network in IoT

doi:10.23940/ijpe.18.10.p4.22802288

Abstract

Abstract:

The increasing popularity of Internet of Things (IoT) technology has greatly influenced the production mode and life quality of humans. Simultaneously, the security issues of such technology have become a focus of attention. There are many aspects of IoT security issues. In this paper, we propose a framework to solve the problem of network intrusion detection in IoT. First, an intrusion detection dataset named UNSW-NB15 is selected as the research object. Then, the dataset is preprocessed and the feature selection job is accomplished to obtain a suitable subset. After the above steps are completed, a Bayesian model is built according to the K2 structure learning algorithm. The parameters are obtained through the Maximum Likelihood Estimation algorithm. Finally, the testing dataset is inputted for classification. The simulation results show that the system can detect the anomaly intrusion effectively.

Submitted on June 21, 2018; Revised on July 13, 2018; Accepted on August 14, 2018
References: 28

Qingping Shi, Jian Kang, Rong Wang, Hang Yi, Yun Lin, and Jie Wang. A Framework of Intrusion Detection System based on Bayesian Network in IoT [J]. Int J Performability Eng, 2018, 14(10): 2280-2288.

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