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Automatic IoT device identification: a deep learning based approach using graphic traffic characteristics

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Abstract

IoT device identification is an effective security measure to track different devices, helping analyze and defend against potential vulnerabilities of various IoT devices. However, existing IoT device identification works mainly use hand-designed features generated from relevant prior knowledge in the field, resulting in additional labor costs, low efficiency, and loss of some potential features. In addition, most of these works only identify known devices in the training set, without considering unknown devices. In this paper, we propose a quick and efficient IoT device identification method. Our method employs the convolutional neural network and converts raw network traffic into images as the model input, automatically extracting features from images instead of manually extracting features. Our method can identifies device types including unknown device types, and detects abnormal traffic of devices. We achieve over 98% accuracy on public datasets with few time consume, demonstrating the accuracy and practicality of our method.

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Funding

This work was supported in part by the Shenzhen Colleges and Universities Stable Support Program No. GXWD20220817124251002, the Joint Funds of the National Natural Science Foundation of China (Grant No. U22A2036), the Fundamental Research Funds for the Central Universities under Grant HIT.OCEF.2021007, the Shenzhen Science and Technology Research and Development Fundation No. JCYJ20190806143418198, and the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies (2022B1212010005).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China

    Shujun Yin, Weizhe Zhang, Yuming Feng & Yang Liu

  2. Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China

    Weizhe Zhang, Yuming Feng & Yang Liu

  3. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, 518055, Guangdong, China

    Weizhe Zhang

  4. School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, 3122, VIC, Australia

    Yang Xiang

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  1. Shujun Yin
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  2. Weizhe Zhang
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  3. Yuming Feng
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  4. Yang Xiang
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  5. Yang Liu
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Correspondence to Weizhe Zhang.

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Yin, S., Zhang, W., Feng, Y. et al. Automatic IoT device identification: a deep learning based approach using graphic traffic characteristics. Telecommun Syst 83, 101–114 (2023). https://doi.org/10.1007/s11235-023-01009-1

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