Robust identification method of website fingerprinting against disturbance
Website fingerprinting usually identifies the target website visited by users based on the website fingerprint characteristics exposed in the web traffic. It is essential in tracking users’ anonymous access behaviors and improving the anonymous traffic governance, especially on Tor network flows. Ho...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2024-12-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024086 |
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| _version_ | 1823864876587548672 |
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| author | ZHANG Jingxi LI Tengyao TU Yukuan LUO Xiangyang |
| author_facet | ZHANG Jingxi LI Tengyao TU Yukuan LUO Xiangyang |
| author_sort | ZHANG Jingxi |
| collection | DOAJ |
| description | Website fingerprinting usually identifies the target website visited by users based on the website fingerprint characteristics exposed in the web traffic. It is essential in tracking users’ anonymous access behaviors and improving the anonymous traffic governance, especially on Tor network flows. However, many defense mechanisms emerged to disturb the distinctive traffic patterns, which results in website fingerprint identification failure. The existing website fingerprint identification method with the best robustness named RF can maintain good performance against various defense methods, but it is difficult to resist the targeted defense method RF Countermeasure. An anti-defense website fingerprinting based on hybrid feature matrix (ADF) was proposed. Unlike RF, ADF used the cumulative packet length instead of the cumulative packet number as the packet-level feature. On the basis of analyzing information leakage value of flow features, ADF constructed the robust flow features of the session level using packet direction distribution and the number of continuous packets in the same direction. Subsequently, a hybrid feature matrix (HFM) was constructed to resist various defense disturbance by combining the features of both packet-level and session-level. With the matrix as input, a robust flow classifier with convolutional neural network was established. Through extensive experimental analysis on the dataset provided by DF, the accuracy under RF Countermeasure is 95.4%, which is 21.2% higher than RF. This method also maintains good identification performance under other state-of-the-art defenses. |
| format | Article |
| id | doaj-art-786e0f29cc4a437892b38364ed3de901 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-786e0f29cc4a437892b38364ed3de9012025-02-08T19:00:09ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2024-12-011013715080361646Robust identification method of website fingerprinting against disturbanceZHANG JingxiLI TengyaoTU YukuanLUO XiangyangWebsite fingerprinting usually identifies the target website visited by users based on the website fingerprint characteristics exposed in the web traffic. It is essential in tracking users’ anonymous access behaviors and improving the anonymous traffic governance, especially on Tor network flows. However, many defense mechanisms emerged to disturb the distinctive traffic patterns, which results in website fingerprint identification failure. The existing website fingerprint identification method with the best robustness named RF can maintain good performance against various defense methods, but it is difficult to resist the targeted defense method RF Countermeasure. An anti-defense website fingerprinting based on hybrid feature matrix (ADF) was proposed. Unlike RF, ADF used the cumulative packet length instead of the cumulative packet number as the packet-level feature. On the basis of analyzing information leakage value of flow features, ADF constructed the robust flow features of the session level using packet direction distribution and the number of continuous packets in the same direction. Subsequently, a hybrid feature matrix (HFM) was constructed to resist various defense disturbance by combining the features of both packet-level and session-level. With the matrix as input, a robust flow classifier with convolutional neural network was established. Through extensive experimental analysis on the dataset provided by DF, the accuracy under RF Countermeasure is 95.4%, which is 21.2% higher than RF. This method also maintains good identification performance under other state-of-the-art defenses.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024086website fingerprinting identificationanonymous traffic classificationhybrid feature matrixrobust fingerprinttraffic analysis |
| spellingShingle | ZHANG Jingxi LI Tengyao TU Yukuan LUO Xiangyang Robust identification method of website fingerprinting against disturbance 网络与信息安全学报 website fingerprinting identification anonymous traffic classification hybrid feature matrix robust fingerprint traffic analysis |
| title | Robust identification method of website fingerprinting against disturbance |
| title_full | Robust identification method of website fingerprinting against disturbance |
| title_fullStr | Robust identification method of website fingerprinting against disturbance |
| title_full_unstemmed | Robust identification method of website fingerprinting against disturbance |
| title_short | Robust identification method of website fingerprinting against disturbance |
| title_sort | robust identification method of website fingerprinting against disturbance |
| topic | website fingerprinting identification anonymous traffic classification hybrid feature matrix robust fingerprint traffic analysis |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024086 |
| work_keys_str_mv | AT zhangjingxi robustidentificationmethodofwebsitefingerprintingagainstdisturbance AT litengyao robustidentificationmethodofwebsitefingerprintingagainstdisturbance AT tuyukuan robustidentificationmethodofwebsitefingerprintingagainstdisturbance AT luoxiangyang robustidentificationmethodofwebsitefingerprintingagainstdisturbance |