A New Website Fingerprinting Method for Tor Hidden Service
Although anonymous communication systems protect user privacy, they also facilitate evasion of network censorship. Currently, evaders use anonymous hidden services to carry out various illegal activities, which pose a serious threat to network management. To address this problem, a new website finge...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818427/ |
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| _version_ | 1832592847775203328 |
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| author | Zihang Hui Jiangtao Zhai Shengxian Wang Weijie Ji |
| author_facet | Zihang Hui Jiangtao Zhai Shengxian Wang Weijie Ji |
| author_sort | Zihang Hui |
| collection | DOAJ |
| description | Although anonymous communication systems protect user privacy, they also facilitate evasion of network censorship. Currently, evaders use anonymous hidden services to carry out various illegal activities, which pose a serious threat to network management. To address this problem, a new website fingerprinting method that combines the Bidirectional Encoder Representations from Transformers (BERT) model and a Long Short-Term Memory (LSTM) network was proposed to improve the accuracy of website fingerprinting for Tor hidden services. The proposed method uses the BERT model to extract the semantic features of webpage content, and the LSTM model is combined to capture the long-term dependencies to achieve efficient recognition of website fingerprints. This method not only deals with the homepage, but also considers the features of sub-pages in depth, which can effectively improve the performance of the model in closed-world and open-world scenarios through deep textual and time-series feature learning. The experimental results show that compared with existing state-of-the-art techniques, the proposed method exhibits better performance in terms of key performance metrics. |
| format | Article |
| id | doaj-art-365a6f2a14ee4e79a31f7a72c54634a4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-365a6f2a14ee4e79a31f7a72c54634a42025-01-21T00:02:19ZengIEEEIEEE Access2169-35362025-01-01138886889710.1109/ACCESS.2024.352392510818427A New Website Fingerprinting Method for Tor Hidden ServiceZihang Hui0https://orcid.org/0009-0009-1044-0919Jiangtao Zhai1https://orcid.org/0000-0001-8557-9899Shengxian Wang2Weijie Ji3Department of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, ChinaDepartment of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaDepartment of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaDepartment of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaAlthough anonymous communication systems protect user privacy, they also facilitate evasion of network censorship. Currently, evaders use anonymous hidden services to carry out various illegal activities, which pose a serious threat to network management. To address this problem, a new website fingerprinting method that combines the Bidirectional Encoder Representations from Transformers (BERT) model and a Long Short-Term Memory (LSTM) network was proposed to improve the accuracy of website fingerprinting for Tor hidden services. The proposed method uses the BERT model to extract the semantic features of webpage content, and the LSTM model is combined to capture the long-term dependencies to achieve efficient recognition of website fingerprints. This method not only deals with the homepage, but also considers the features of sub-pages in depth, which can effectively improve the performance of the model in closed-world and open-world scenarios through deep textual and time-series feature learning. The experimental results show that compared with existing state-of-the-art techniques, the proposed method exhibits better performance in terms of key performance metrics.https://ieeexplore.ieee.org/document/10818427/BERT modelencrypted traffic detectionhidden servicesLSTM networkwebsite fingerprinting |
| spellingShingle | Zihang Hui Jiangtao Zhai Shengxian Wang Weijie Ji A New Website Fingerprinting Method for Tor Hidden Service IEEE Access BERT model encrypted traffic detection hidden services LSTM network website fingerprinting |
| title | A New Website Fingerprinting Method for Tor Hidden Service |
| title_full | A New Website Fingerprinting Method for Tor Hidden Service |
| title_fullStr | A New Website Fingerprinting Method for Tor Hidden Service |
| title_full_unstemmed | A New Website Fingerprinting Method for Tor Hidden Service |
| title_short | A New Website Fingerprinting Method for Tor Hidden Service |
| title_sort | new website fingerprinting method for tor hidden service |
| topic | BERT model encrypted traffic detection hidden services LSTM network website fingerprinting |
| url | https://ieeexplore.ieee.org/document/10818427/ |
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