ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (11): 2374-2399.doi: 10.7544/issn1000-1239.2021.20210676

Special Issue: 2021密码学与网络空间安全治理专题

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Hybrid Feature Fingerprint-Based Wireless Device Identification

Song Yubo1,2,3, Chen Bing1,2,3,4, Zheng Tianyu1,2,3, Chen Hongyuan1,2,3, Chen Liquan1,2,3, Hu Aiqun3,4   

  1. 1（School of Cyber Science and Engineering, Southeast University, Nanjing 211189);2(Key Laboratory of Computer Network Technology of Jiangsu Province (Southeast University), Nanjing 210096);3(Purple Mountain Laboratories, Nanjing 211111);4(School of Information Science and Engineering, Southeast University, Nanjing 211189)
  • Online:2021-11-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2020YFE0200600).

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Abstract: Wireless networks transmit data over open wireless channels, so they are vulnerable to impersonation attacks and information forgery attacks. To prevent such attacks, accurate device identification is required. The device identification technology based on channel state information (CSI) fingerprinting uses the wireless channel characteristics of device for identification. Since CSI can provide fine-grained channel characteristics and can be easily obtained from OFDM wireless devices, this technology has received wide attention. However, since CSI fingerprints identify the wireless channel characteristics of device, they change with the location or the environment of device. What’s more, the existing technologies usually use machine learning for fingerprint matching for increasing identification accuracy, but the computational complexity of fingerprint matching increases, which in turn cannot be implemented in embedded devices with limited computational ability. To address these problems, this paper proposes a hybrid feature fingerprint-based device identification scheme, which includes the identification in access stage and communication stage. Packet arrival interval distribution (PAID) fingerprint, which is independent of device’s location, is introduced for identification in access stage to compensate for the shortcomings of the CSI fingerprint. In communication stage, CSI fingerprints are extracted from each data packet and identified in real time with the feature that CSI can be acquired packet by packet. In addition, this paper proposes a fingerprint matching scheme with low computational complexity to ensure fast and accurate device identification even in devices with limited computational ability. We implement the identification system on Raspberry Pi and perform some experiments, which show that the identification accuracy is up to 98.17% and 98.7% in access stage and communication stage, and the identification time of a single packet in communication stage is only 0.142ms.

Key words: wireless network security, wireless device identification, hybrid feature fingerprint, channel state information (CSI), autoencoder

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