Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning
In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it atta...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Walter de Gruyter GmbH
2021
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| Online Access: | https://hdl.handle.net/1721.1/129817 |
| _version_ | 1826213503921291264 |
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| author | Bhat, Sanjit Lu, David Kwon, Albert Hyukjae Devadas, Srinivas |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Bhat, Sanjit Lu, David Kwon, Albert Hyukjae Devadas, Srinivas |
| author_sort | Bhat, Sanjit |
| collection | MIT |
| description | In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over 1% higher true positive rate (TPR) than state-of-the-art attacks while achieving 4× lower false positive rate (FPR). Var-CNN’s improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by 3.12% while increasing the TPR by 13%. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues. |
| first_indexed | 2024-09-23T15:50:17Z |
| format | Article |
| id | mit-1721.1/129817 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:50:17Z |
| publishDate | 2021 |
| publisher | Walter de Gruyter GmbH |
| record_format | dspace |
| spelling | mit-1721.1/1298172022-09-29T16:28:57Z Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning Bhat, Sanjit Lu, David Kwon, Albert Hyukjae Devadas, Srinivas Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over 1% higher true positive rate (TPR) than state-of-the-art attacks while achieving 4× lower false positive rate (FPR). Var-CNN’s improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by 3.12% while increasing the TPR by 13%. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues. National Science Foundation (U.S.) (Grant 1813087) 2021-02-18T16:19:49Z 2021-02-18T16:19:49Z 2019-07 2019-06 2020-12-10T17:33:36Z Article http://purl.org/eprint/type/ConferencePaper 2299-0984 https://hdl.handle.net/1721.1/129817 Bhat, Sanjit et al. “Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning.” Proceedings on Privacy Enhancing Technologies, 2019, 4 (July 2019): 292–310 © 2019 The Author(s) en 10.2478/POPETS-2019-0070 Proceedings on Privacy Enhancing Technologies Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Walter de Gruyter GmbH Sciendo |
| spellingShingle | Bhat, Sanjit Lu, David Kwon, Albert Hyukjae Devadas, Srinivas Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning |
| title | Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning |
| title_full | Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning |
| title_fullStr | Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning |
| title_full_unstemmed | Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning |
| title_short | Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning |
| title_sort | var cnn a data efficient website fingerprinting attack based on deep learning |
| url | https://hdl.handle.net/1721.1/129817 |
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