Browser fingerprint linking based on transformer-encoder model

Yun Tan; Xiaoyong Li; Xiaotian Si; Linghui Li; Yali Gao; Jie Yuan

doi:10.1117/12.2683201

26 June 2023 Browser fingerprint linking based on transformer-encoder model

Yun Tan, Xiaoyong Li, Xiaotian Si, Linghui Li, Yali Gao, Jie Yuan

Proceedings Volume 12714, International Conference on Computer Network Security and Software Engineering (CNSSE 2023); 127141M (2023) https://doi.org/10.1117/12.2683201
Event: Third International Conference on Computer Network Security and Software Engineering (CNSSE 2023), 2023, Sanya, China

Abstract

Browser fingerprinting has been used as a user-tracking technique in recent years. As a long-term tracking technique, it requires not only obtaining unique browser fingerprints but also linking fingerprints from the same browser instance in that browsers change rapidly and frequently. To improve the efficiency of linking the evolving browser fingerprints, in this paper, we propose a browser fingerprint linking method based on Transformer-encoder. Transformer-encoder utilizes an attention mechanism to focus on certain parts of the input sequence, enabling it to capture complex connections and interactions within the data more efficiently. To make the most of the parallel processing mechanism of the Transformer-encoder, we combine multiple fingerprint comparison vectors into an input vector to train the model. We conduct extensive experiments on a public dataset to evaluate our proposed model. The experimental results show that our model outperforms some existing models, which proves the effectiveness of the Transformer-encoder in linking browser fingerprints.

Citation Download Citation

Yun Tan, Xiaoyong Li, Xiaotian Si, Linghui Li, Yali Gao, and Jie Yuan "Browser fingerprint linking based on transformer-encoder model", Proc. SPIE 12714, International Conference on Computer Network Security and Software Engineering (CNSSE 2023), 127141M (26 June 2023); https://doi.org/10.1117/12.2683201

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