Netinfo Security ›› 2021, Vol. 21 ›› Issue (9): 67-73.doi: 10.3969/j.issn.1671-1122.2021.09.010

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IoT Device Recognition Model Based on Depthwise Separable Convolution

CHEN Qinggang1, DU Yanhui1(), HAN Yi1,2, LIU Xiangyu1   

  1. 1. College of Information Network Security, People’s Public Security University of China, Beijing 100038, China;
    2. The First Research Institute of the Ministry of Public Security, Beijing 100048, China
  • Received:2021-06-02 Online:2021-09-10 Published:2021-09-22
  • Contact: DU Yanhui E-mail:dyh6889@126.com

Abstract:

With the continuous growth of the number of IoT devices, the problem of IoT device management has become increasingly prominent. How to accurately identify IoT devices in the resource-limited IoT environment is a key problem to be solved urgently. To solve the difficulty in extracting the traffic features of devices in the Internet of Things (IoT), an Internet of Things device identification method based on deep separable convolution was proposed. In this method, device fingerprints were constructed using payload data at session granularity, and depth features were extracted from device fingerprints through convolutional layer. Experimental results show that this method can effectively identify device types with limited resources. Compared with the standard CNN method and manual feature extraction technique, the overall performance is improved.

Key words: Internet of Things device, flow characteristics, separable convolution, device fingerprinting

CLC Number: