High Precision Open-World Website Fingerprinting
High Precision Open-World Website Fingerprinting
复制标题
高精度开放世界网站指纹识别
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Tao Wang
中科院分区：
文献类型：
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作者：
Tao Wang
流量分析攻击仅使用她的数据包元数据-称为网站指纹(WF)-识别客户正在浏览的网页，在针对Tor等隐私技术的封闭实验中已被证明有效。我们想要调查它们在真实的开放世界中的用处。一些WF攻击声称具有高召回率和低假阳性率，但它们只有在高基准率页面上才被证明是成功的。我们明确地将基本利率并入精度，并将其称为r-精度。利用这一度量，我们证明了当基准率较低时，以前最好的攻击具有较低的精度；我们研究了这样的场景(r=1000)，其中最大r-精度仅达到0.14。为了提高r-精度，我们提出了三类新的精度优化器，它们可以应用于任何分类器来提高精度。当r=1000时，我们最优的分类器可以达到至少0.86的精度，精度提高了6倍以上。第一次，我们展示了一个WF分类器，它可以扩展到任何开放世界集的大小。我们还调查了使用精确分类器来解决网站指纹识别中的现实目标，包括不同类型的网站，识别敏感客户，以及击败网站指纹识别防御。
Traffic analysis attacks to identify which web page a client is browsing, using only her packet metadata — known as website fingerprinting (WF) — has been proven effective in closed-world experiments against privacy technologies like Tor. We want to investigate their usefulness in the real open world. Several WF attacks claim to have high recall and low false positive rate, but they have only been shown to succeed against high base rate pages. We explicitly incorporate the base rate into precision and call it r-precision. Using this metric, we show that the best previous attacks have poor precision when the base rate is realistically low; we study such a scenario (r = 1000), where the maximum r-precision achieved was only 0.14.To improve r-precision, we propose three novel classes of precision optimizers that can be applied to any classifier to increase precision. For r = 1000, our best optimized classifier can achieve a precision of at least 0.86, representing a precision increase by more than 6 times. For the first time, we show a WF classifier that can scale to any open world set size. We also investigate the use of precise classifiers to tackle realistic objectives in website fingerprinting, including different types of websites, identification of sensitive clients, and defeating website fingerprinting defenses.