IoT-IE: An Information-Entropy-Based Approach to Traffic Anomaly Detection in Internet of Things

Author:

Sun Yizhen12,Yu Jianjiang3,Tian Jianwei12,Chen Zhongwei12,Wang Weiping3,Zhang Shigeng34ORCID

Affiliation:

1. State Grid Information & Communication Company of Hunan Electric Power Corporation, Changsha, China

2. Hunan Key Laboratory for Internet of Things in Electricity, Changsha 410004, China

3. School Computer Science and Engineering, Central South University, Changsha 410012, China

4. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

Abstract

Security issues related to the Internet of Things (IoTs) have attracted much attention in many fields in recent years. One important problem in IoT security is to recognize the type of IoT devices, according to which different strategies can be designed to enhance the security of IoT applications. However, existing IoT device recognition approaches rarely consider traffic attacks, which might change the pattern of traffic and consequently decrease the recognition accuracy of different IoT devices. In this work, we first validate by experiments that traffic attacks indeed decrease the recognition accuracy of existing IoT device recognition approaches; then, we propose an approach called IoT-IE that combines information entropy of different traffic features to detect traffic anomaly. We then enhance the robustness of IoT device recognition by detecting and ignoring the abnormal traffic detected by our approach. Experimental evaluations show that IoT-IE can effectively detect abnormal behaviors of IoT devices in the traffic under eight different types of attacks, achieving a high accuracy value of 0.977 and a low false positive rate of 0.011. It also achieves an accuracy of 0.969 in a multiclassification experiment with 7 different types of attacks.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Information Systems

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