Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (7): 317-324.doi: 10.11896/jsjkx.220600068

• Information Security • Previous Articles     Next Articles

Browser Fingerprint Recognition Based on Improved Self-paced Ensemble Algorithm

ZHANG Desheng1, CHEN Bo2, ZHANG Jianhui2, BU Youjun2, SUN Chongxin2, SUN Jia1   

  1. 1 School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou,450000,China
    2 Information Technology Institute,PLA Strategic Support Force Information Engineering University,Zhengzhou 450000,China
  • Received:2022-06-07 Revised:2022-10-14 Online:2023-07-15 Published:2023-07-05
  • About author:ZHANG Desheng,born in 1997,postgraduate.His main research interests include cyberspace security and so on.ZHANG Jianhui,born in 1977,Ph.D,associate researcher,master supervisor.His main research interests include new network architecture,network routing technology,network data analysis and security control.
  • Supported by:
    National Natural Science Foundation of China(62176264).

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Abstract: Browser fingerprinting technology has been used by many websites for user tracking,advertising delivery and security verification due to its stateless,cross-domain consistency and other advantages.The task of browser fingerprint recognition is a typical classification task of imbalanced data.The data imbalance exists in browser fingerprint long-term tracking task,which will lead to low accuracy of fingerprint recognition and failure of long-term tracking.An improved Self-paced Ensemble(ISPE) method is proposed to identify browser fingerprints.And the undersampling process of browser fingerprint sample and the training process of single classifier in ensemble learning are improved.Focusing on the browser fingerprint which is difficult to identify,added attention-like mechanism and self-paced factor are optimized to make the classifier pay more attention to the boundary samples which are difficult to classify in the training process,to improve the overall accuracy of browser fingerprint recognition.The results show that the F1-score of ISPE algorithm for browser fingerprint recognition reaches 95.6%,which is 16.8% higher than that of Bi-RNN algorithm.It proves that the method has excellent performance for long-term browser fingerprint tracking.

Key words: Browser fingerprinting, User tracking, Self-paced Ensemble, Undersampling, Ensemble learning

CLC Number: 

  • TP393
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