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This can damage the user privacy provided by anonymity systems such as Tor. Tor has implemented the WF defense called Circuit Padding Framework, which provides an interface for developers to implement their own defenses. However, these defenses in the framework were overcome by the Deep Fingerprinting (DF) attack. In this paper, we propose a novel defense approach called break burst padding (Break-Pad), which injects a random number of padding packets into an incoming burst once the number of consecutive incoming packets exceeds a set number. We integrated Break-Pad into the existing Circuit Padding Framework. In addition, we have implemented two padding machines named August and October in the new framework and conducted experiments to evaluate these machines. In the open-world setting, our results show that August, with 29% bandwidth overhead, reduces Tik-Tok\u2019s TPR by 14.48% and DF\u2019s TPR by 22%. October outperforms the best padding machine, RBB. With 36% bandwidth overhead, it drops Tik-Tok\u2019s TPR to 74.24% and DF\u2019s TPR to 65.36%. In the one-page setting, October further reduces the bandwidth overhead by 11% while achieving similar performance to RBB. In the information leak analysis, for the burst sequence feature of the traffic, October leaks at 2.453 bits, while the best comparable padding machine Interspace leaks at 2.629 bits.<\/jats:p>","DOI":"10.1186\/s42400-024-00222-y","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T02:01:51Z","timestamp":1727748111000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Break-Pad: effective padding machines for tor with break burst padding"],"prefix":"10.1186","volume":"7","author":[{"given":"Bin","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanhui","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"222_CR1","doi-asserted-by":"publisher","unstructured":"Abusnaina A, Jang RHO, Khormali A et\u00a0al (2020) DFD: adversarial learning-based approach to defend against website fingerprinting. 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