who have access to the communications between the user and their connection to the Internet, or on an anonymity system like Tor, the entry guard (see the figure below). There are many entities in a position to access this communication including wifi router owners, local network administrators or eavesdroppers, Internet Service Providers, and Autonomous Systems, among other network intermediaries.Prior studies typically report average performance results for a given website fingerprinting method or countermeasure. However, if you own a hidden service, you are more concerned with the security of your particular hidden service than how well an attack or defense works overall. If your site is naturally hidden against attacks, then you do not need to implement a defense. Conversely, your site may not be protected by a certain defense, despite the high overall protection of such defense.In this study, we try to answer the following two questions:Are some websites more fingerprintable than others?If so, what makes them more (or less) fingerprintable?Disparate impact of website fingerprintingWe have identified high variance in the results obtained by the website fingerprinting state-of-the-art attacks (i.e., k-NN, CUMUL and k-FP) across different onion websites: some sites (such as the ones in the table below) have higher identification rates than others and, thus, are more vulnerable to website fingerprinting.The table below shows the top five onion services ranked by number of misclassifications. We observe a partial overlap between the sites that are most misclassified across different classifiers. This indicates the errors of these classifiers are correlated to some extent. We looked into these classifications in more detail..onion URLTPFPFNF1k-NN4fouc...484660.05ykrxn...362670.04wiki5k...377670.04ezxjj...276680.03newsi...187690.01CUMULzehli...215680.054ewrw...229680.04harry...229680.04sqtlu...235680.04yiy4k...114690.02k-FPykrxn...462660.06ykrxn...342670.05wiki5...355670.05jq77m...254680.03newsi...263680.03
Analysis of classification errorsWe have analyzed the misclassifications of the three state-of-the-art classifiers. In the following Venn diagram, each circle represents the set of prediction errors for one of the classifiers. In the intersections of these circles are the instances that were incorrectly classified by the overlapping methods. 31% of the erred instances were misclassified by all three methods, suggesting strong correlation in the errors.We looked into the misclassifications that fall in the intersection among the three classifiers to understand what features make them be consistently misclassified.Misclassification graphConfusion graph for the CUMUL classifier drawn by Gephi software using the methodology explained in the paper. Nodes are colored based on the community they belong to, which is determined by the Louvain community detection algorithm. Node size is drawn proportional to the node degree, that is, bigger node means lower classification accuracy. We observe highly connected communities on the top left, and the right which suggests clusters of Hidden Services which are commonly confused as each other. Further, we notice several node pairs that are commonly classified as each other, forming ellipses.Network-level featuresIn the figure below we plot the instances that fall in the intersection of the misclassification areas of the attacks in the Venn diagram. In the x-axis we plot the normalized median incoming size of the true site and, in the y-axis, we show the same feature for the site that the instance was confused with.Total incoming packet size can be thought as the size of the site, as most traffic in a web page download is incoming.We see that the sizes of the true and the predicted sites in the misclassifications are strongly correlated, indicating that sites that were misclassified had similar sizes.At the same time, the high density of instances (see the histograms at the margins of the figure) shows that the vast majority of sites that were misclassified are small.Site-level featuresThe figure below shows the results of the site-level feature analysis using information gain as feature importance metric. We see that features associated with the size of the site give the highest information gain for determining fingerprintability when all the sites are considered. Among the smallest sites, which are generally less identifiable, we see that standard deviation features are also important, implying that sites that are more dynamic are harder to fingerprint.ConclusionsWe have studied what makes certain sites more or less vulnerable to the attack. We examine which types of features are common in sites vulnerable to website fingerprinting attacks. We also note that from the perspective of an onion service provider, overall accuracies do not matter, only whether a particular defense will protect their site and their users.Our results can guide the designers and operators of onion services as to how to make their own sites less easily fingerprintable and inform design decisions for countermeasures, in particular considering the results of our feature analyses and misclassifications. For example, we show that the larger sites are reliably more identifiable, while the hardest to identify tend to be small and dynamic.. This includes crawling infrastructure, modules for analysing browser profile data and crawl datasets.
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