ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (11): 2294-2311.doi: 10.7544/issn1000-1239.2020.20200459

Special Issue: 2020密码学与数据隐私保护研究专题

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Android Browser Fingerprinting Identification Method Based on Bidirectional Recurrent Neural Network

Liu Qixu1,2, Liu Xinyu1,2, Luo Cheng3, Wang Junnan1,2, Chen Langping1,2, Liu Jiaxi1,2   

  1. 1(Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093);2(School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100049);3(China Academy of Information and Communications Technology, Beijing 100191)
  • Online:2020-11-01
  • Supported by: 
    This work was supported by the Youth Innovation Promotion Association of Chinese Academy of Sciences (2019163), the National Natural Science Foundation of China (61902396), the Strategic Priority Research Program of Chinese Academy of Sciences (XDC02040100), and the Project of the Key Laboratory of Network Assessment Technology at Chinese Academy of Sciences and Beijing Key Laboratory of Network Security and Protection Technology.

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Abstract: Browser fingerprinting is a user identification method which has gradually matured since its concept was proposed in 2010 and is widely used in a lot of popular business websites to serve ads accurately. However, traditional fingerprinting has lots of problems in tracing users because it changes subtly no matter if the fingerprint feature value is changed due to system upgrade, browser update or tampering caused by fingerprint blocker. On the basis of research on the attributes of browser fingerprint, a great number of fingerprints from the volunteers who used Android devices are collected and supervised learning framework RNNBF for user identification is proposed. The robustness of RNNBF is reflected in the data and the model respectively. In the data aspect, the fingerprint-based data enhancement technology is used to generate the enhanced data set. In the model aspect, the attention mechanism is used to make our model focus more on the invariant fingerprint features. In terms of model evaluation, the RNNBF model is compared with the single-layer LSTM model and the random forest model. When F1-Score is used as the evaluation standard, the recognition effect of the RNNBF model is better than the latter two, which proves the excellent performance of RNNBF in dynamically linking fingerprints.

Key words: browser fingerprinting, Android, data augmentation, recurrent neural network, progressive Web Apps

CLC Number: