Tor onion site
Простота, удобство, возможность выбора гарантов и фокус на анонимности и безопасности - их фишка. Onion - Candle, поисковик по Tor. Onion - Нарния клуб репрессированных на рампе юзеров. Onion - Valhalla удобная и продуманная площадка на англ. 3.7/5 Ссылка TOR зеркало Ссылка https probiv. Onion/ Shkaf (бывшая Нарния) Шкаф Подпольное сообщество людей, которые любят брать от жизни максимум и ценят возможность дышать полной грудью. Маркет был вновь запущен в апреле 2021 года с новым дизайном и движком. Внезапно много русских пользователей. Регистрация стоит 100, но в отличии от «Верифа существует и бесплатный вариант. Пользуйтесь на свой страх и риск. Onion - простенький Jabber сервер в торе. Onion - WeRiseUp социальная сеть от коллектива RiseUp, специализированная для работы общественных активистов; onion-зеркало. Onion - Mail2Tor, e-mail сервис. Onion - Архив Хидденчана архив сайта hiddenchan. Kp6yw42wb5wpsd6n.onion - Minerva зарубежная торговая площадка, обещают некое двойное шифрование ваших данных, присутствует multisig wallets, саппорт для разрешения ситуаций. Торрент трекеры, библиотеки, архивы. Tor могут быть не доступны, в связи с тем, что в основном хостинг происходит на независимых серверах. Onion/ - форум FreeHacks Ссылка удалена hydra по притензии роскомнадзора Ссылка удалена по притензии роскомнадзора Сообщения, Анонимные Ящики (коммуникации) Сообщения, анонимные ящики (коммуникации) bah37war75xzkpla. А какие же случаи уже случались не только с самим даркнетом, а именно с его пользователями? Что такое теневые сайты? Проверка обменных пунктов, осуществляемая BestChange при включении в мониторинг, выполняется по множеству параметров и доказала свою эффективность. Является зеркалом сайта fo в скрытой сети, проверен временем и bitcoin-сообществом. Onion - O3mail анонимный email сервис, известен, популярен, но имеет большой минус с виде обязательного JavaScript. Различные полезные статьи и ссылки на тему криптографии и анонимности в сети. В платных аках получше. Onion - XmppSpam автоматизированная система по спаму в jabber. Английский язык. 3.6/5 Ссылка TOR зеркало Ссылка TOR зеркало http rms26hxkohmxt5h3c2nruflvmerecwzzwbm7chn7d3wydscxnrin5oad. One TOR зеркало https monza73jr6otjiycgwqbym2qadw8il. Недавно переименовались в shkaf. Зарубежный форум соответствующей тематики. Onion - Bitcoin Blender очередной биткоин-миксер, который перетасует ваши битки и никто не узнает, кто же отправил их вам.
Tor onion site - Ссылка на сайт hydra в тор браузере
What is a .onion domain and how does it workA .onion domain is the address of a website that can only be accessed through the Tor anonymity browser. Regular browsers won’t be able to navigate the relay of proxy servers that will take users to your website.How is it different from an ordinary domain?Ordinary web domains, like .com, .org, .biz, and others are issued by the Internet Corporation for Assigned Names and Numbers (ICANN). There are thousands of different domains out there, but not all of them can be used by everyone (like .apple, for example). Users have to submit proposals to ICANN to register a domain and sub-domain (the part before the period). There are usually costs associated with registering and maintaining the domain of your choice.Why would I want a .onion address?A .onion domain has a few key advantages over an ordinary domain (but a few drawbacks as well). Its key feature – that it can only be accessed using a Tor browser – is both a drawback and an advantage. Tor is far from the most popular browser, and many people don’t even know it exists, so you shouldn’t expect massive traffic on your .onion site. However, the Tor browser affords numerous layers of anonymity that are not available on more popular browsers. If you want to ensure near-total anonymity for both you and your visitors, you can’t do much better than a Tor address.When you create a .onion site, a domain name will automatically be generated for you. It will be a string of 16 random lowercase letters and numbers (from 2 to 7) that the Tor browser can use to navigate to your server. Unfortunately, these random strings cannot be any longer or shorter than 16 characters and are often hard to remember, making it difficult for users to memorize your website and easy for malicious users to create a similar but different domain to potentially confuse visitors.However, this also means that you do not need to register with ICANN to create your own domain. You won’t need to hide your details from “whois” searches, and your ICANN account won’t be vulnerable to malicious takeovers. You will be completely in control of your privacy and your domain.Creating a vanity domain – one featuring a recognizable word of your choice – is possible but computationally expensive. Facebook devoted considerable resources to achieving its .onion domain – facebookcorewwwi.onion – and they only needed 8 characters. Getting the exact 16 characters you want could take a single computer billions years to achieve.How do I create a .onion domain?1. Create a web serverTor’s .onion service can give your existing web server a .onion domain if it’s configured correctly. However, the powerful anonymity provided by Tor isn’t worth much if your server leaks personal data or information that advanced users could use to identify you. Tor suggests binding your server to localhost. When you set up your .onion services later, you’ll create a virtual port that visitors can connect through so you don’t reveal your real IP address.Make sure you also scrub your server of any other information that might identify you, your IP, or your location. Remove any reference to your server’s information from any error messages that might be sent to visitors.2. Configure your server’s .onion servicesTo do this, you’ll have to open your “torrc” file, which is a text file you received when you set up your Tor browser. For more detailed information on how to modify this file to create a .onion server, follow the instructions on the Tor project’s website.Once your setup is complete, turn on your Tor browser to generate a public key, or domain, for your website. After that, it’s up to you to distribute it and get people to visit your site. Just be sure not to share the private key with anyone!
The study is a collaboration between researchers Rebekah Overdorf1, Marc Juarez2, Gunes Acar2, Rachel Greenstadt1, Claudia Diaz2
1 Drexel University {rebekah.overdorf,rachel.a.greenstadt}@drexel.edu
2 imec-COSIC KU Leuven {marc.juarez, gunes.acar, claudia.diaz}@esat.kuleuven.be
Reference: R. Overdorf, M. Juarez, G. Acar, R. Greenstadt, C. Diaz. How Unique is Your .onion? An Analysis 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.