App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 6

Incremental Tor website fingerprinting via boundary sample-prioritized replay

Xie Xiaoran
Cai Manchun
Wang Longhao
Tian Yuzheng
Institute of Information and Network Security, People's Public Security University of China, Beijing 100038, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0402

Abstract

In response to the emergence of new sites on the Tor network, existing website fingerprinting models face significant challenges from concept drift and catastrophic forgetting. To address these issues, we propose an incremental recognition method for Tor website fingerprinting that utilizes a prioritized replay of boundary samples. We establish a Boundary Sample Selection (BSS) Strategy that models the distribution of traffic features to identify and preferentially replay samples crucial for maintaining the decision boundary. Concurrently, our approach integrates a Proxy Contrastive Learning (PCL) mechanism with an enhanced graph construction method based on Time-Window Augmentation (TWA) to reinforce the stability of class representations. Furthermore, we employ a classifier that dynamically expands to accommodate newly emerging websites. Experiments on a Tor website-fingerprint dataset reflecting real user behavior demonstrate that, in Top-100 and Top-300 incremental recognition settings, our proposed method achieves final accuracies of 93.71% and 87.16%, respectively. This performance outperforms mainstream incremental learning approaches by more than 3.2 percentage points and exhibits notable advantages on key metrics such as accuracy and a lower forgetting rate. These results indicate that the method effectively mitigates concept drift and catastrophic forgetting in dynamic open-world scenarios, offering a generalizable technical pathway for the continuous monitoring of Tor website fingerprints.

Foundation Support

高等学校学科创新引智基地资助项目(B20087)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0402
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 6

Publish History

[2026-01-30] Accepted Paper

Cite This Article

谢翛然, 蔡满春, 王珑皓, 等. 边界样本优先重放的Tor网站指纹增量识别方法 [J]. 计算机应用研究, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0402. (Xie Xiaoran, Cai Manchun, Wang Longhao, et al. Incremental Tor website fingerprinting via boundary sample-prioritized replay [J]. Application Research of Computers, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0402. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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