Tor onion site

By Philipp Winter, Annie Edmundson, Laura Roberts, Agnieskza Dutkowska-Żuk, Marshini Chetty, and Nick FeamsterWant to find US military drone data leaks online? Frolick in a fraudster’s paradise for people’s personal information? Or crawl through the criminal underbelly of the Internet? These are the images that come to most when they think of the dark web and a quick google search for “dark web” will yield many stories like these. Yet, far less is said about how the dark web can actually enhance user privacy or overcome censorship by enabling anonymous browsing through Tor. Recently, for example, Brave, dedicated to protecting user privacy, integrated Tor support to help users surf the web anonymously from a regular browser. This raises questions such as: is the dark web for illicit content and dealings only? Can it really be useful for day-to-day web privacy protection? And how easy is it to use anonymous browsing and dark web or “onion” sites in the first place?To answer some of these pressing questions, we studied how Tor users use onion services. Our work will be presented at the upcoming USENIX Security conference in Baltimore next month and you can read the full paper here or the TLDR version here.What are onion services?: Onion services were created by the Tor project in 2004. They not only offer privacy protection for individuals browsing the web but also allow web servers, and thus websites themselves, to be anonymous. This means that any “onion site” or dark web site cannot be physically traced to identify those running the site or where the site is hosted. Onion services differ from conventional web services in four ways. First, they can only be accessed over the Tor network. Second, onion domains, (akin to URLs for the regular web), are hashes over their public key and consist of a string of letters and numbers, which make them long, complicated, and difficult to remember. These domains sometimes contain prefixes that are human-readable but they are expensive to generate (e.g. torprojectqyqhjn.onion). We refer to these as vanity domains. Third, the network path between the client and the onion service is typically longer, meaning slower performance owing to longer latencies. Finally, onion services are private by default, meaning that to find and use an onion site, a user has to know the onion domain, presumably by finding this information organically, rather than with a search engine.What did we do to investigate how Tor users make use of onion services?: We conducted a large scale survey of 517 Tor users and interviewed 17 Tor users in depth to determine how users perceive, use, and manage onion services and what challenges they face in using these services. We asked our participants about how they used Tor’s onion services and how they managed onion domains. In addition, we asked users about their expectations of privacy and their privacy and security concerns when using onion services. To compliment our qualitative data, we analyzed “leaked” DNS lookups to onion domains, as seen from a DNS root server. This data gave us insights into actual usage patterns to corroborate some of the findings from the interviews and surveys. Our final sample of participants were young, highly educated, and comprised of journalists, whistleblowers, everyday users wanting to protect their privacy to those doing competitive research on others and wanting to avoid being “outed”. Other participants included activists and those who wanted to avoid government detection for fear of persecution or worse.What were the main findings? First, unsurprisingly, onion services were mostly used for anonymity and security reasons. For instance, 71% of survey respondents reported using onion services to protect their identity online. Almost two thirds of the survey respondents reported using onion services for non-browsing activities such as TorChat, a secure messaging app built on top of onion services. 45% of survey participants had other reasons for using Tor such as to help educate users about the dark web or for their personal blogs. Only 27% of survey respondents reported using onion services to explore the dark web and its content “out of curiosity”.Second, users had a difficult time finding, tracking, and saving onion links. Finding links: Almost half of our survey respondents discovered onion links through social media such as Twitter or Reddit or by randomly encountering links while browsing the regular web. Fewer survey respondents discovered links through friends and family. Challenges users mentioned for finding onion services included:Onion sites frequently change addresses and so often onion domain aggregators have broken and out of date links.Unlike traditional URLS, onion links give no indication of the content of the website so it is difficult to avoid potentially offensive or illicit content.Again, unlike traditional URLS, participants said it is hard кракен to determine through a glance at the address bar if a site is the authentic one you are trying to reach instead of a phishing site.A frequent wish expressed by participants was for a better search engine that is more up to date and gives an indication of the content before one clicks on the link as well as authenticity of the site itself.Tracking and Saving links: To track and save complicated onion domains, many participants opted to bookmark links but some did not want to leave a trace of websites they visited on their machines. The majority of other survey respondents had ad-hoc measures to deal with onion links. Some memorized a few links and did so to protect privacy by not writing the links down. However, this was only possible for a few vanity domains in most cases. Others just navigated to the places where they found the links in the first place and used the links from there to open the websites they needed.Third, onion domains are also hard to verify as authentic. Vanity domains: Users appreciated vanity domains where onion services operators have taken extra effort and expense to set up a domain that is almost readable such as the case of Facebook’s onion site, facebookcorewwwi.onion. Many participants liked the fact that vanity domains give more indication of the content of the domain. However, our participants also felt vanity domains could lead to more phishing attacks since people would not try to verify the entire onion domain but only the readable prefix. “We also get false expectations of security from such domains. Somebody can generate another onion key with same facebookcorewwwi address. It’s hard but may be possible. People who believe in uniqueness of generated characters, will be caught and impersonated.” – Participant S494Verification Strategies: Our participants had a variety of strategies such as cutting and pasting links, using bookmarks, or verifying the address in the address bar to check the authenticity of a website. Some checked for a valid HTTPS certificate or familiar images in the website. However, a over a quarter of our survey respondents reported that they could not tell if a site was authentic (28%) and 10% did not even check for authenticity at all. Some lamented this is innate to the design of onion services and that there is not real way to tell if an onion service is authentic epitomized by a quote from Participant P1: “I wouldn’t know how to do that, no. Isn’t that the whole point of onion services? That people can run anonymous things without being able to find out who owns and operates them?”Fourth, onion lookups suggest typos or phishing. In our DNS dataset, we found similarities between frequently visited popular onion sites such as Facebook’s onion domain and similar significantly less frequently visited websites, suggesting users were making typos or potentially that phishing sites exist. Of the top 20 onion domains we encountered in our data set, 16 were significantly similar to at least one other onion domain in the data set. More details are available in the paper.What do these findings mean for Tor and onion services? Tor and onion services do have a part to play in helping users to protect their anonymity and privacy for reasons other than those usually associated with a “nefarious” dark web such as support for those overcoming censorship, stalking, and exposing others’ wrong-doing or whistleblowing. However, to better support these uses of Tor and onion services, our users wanted onion service improvements. Desired improvements included more support for Tor in general in browsers, improvement in performance, improved privacy and security, educational resources on how to use Tor and onion services, and finally improved onion services search engines. Our results suggest that to enable more users to make use of onion services, users need:better security indicators to help them understand Tor and onion services are working correctlyautomatic detection of phishing in onion servicesopt in publishing of onion domains to improve search for legitimate and legal contentbetter ways to track and save onion links including privacy preserving onion bookmarking.Future studies to further demystify the dark web are warranted and in our paper we make suggestions for more work to understand the positive aspects of the dark web and how to support privacy protections for everyday users.You can read more about our study and its limitations here (such as the fact our participants were self-selected and may not represent those who do use the dark web for illicit activities for instance) or skim the paper summary.

Tor onion site - Открытая ссылка крамп krmp.cc

of the Fingerprintability of Tor Onion Services . In Proceedings of ACM Conference on Computer and Communications Security (CCS'17). ACM, Nov. 2017. (Forthcoming)Website fingerprinting attacks aim to uncover which web pages a target user visits. They apply supervised machine learning classifiers to network traffic traces to identify patterns that are unique to a web page. These attacks circumvent the protection afforded by encryption and the metadata protection of anonymity systems such as Tor.Website fingerprinting can be deployed by adversaries with modest resources 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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