Перейти на Kraken Вход на Kraken через TOR Вход на Kraken Telegram зеркало кракен kraken12.at kraken13.at кракен зайти на сайт

Tor onion site

Tor onion site

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 актуальная 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 ссылка 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.

Tor onion site - Настоящий сайт крамп krmp.cc

Org b Хостинг изображений, сайтов и прочего Хостинг изображений, сайтов и прочего matrixtxri745dfw. Реальные отзывы дадут вам понять кто из них ТОП. Сайты сети TOR, поиск в darknet, сайты Tor. Whisper4ljgxh43p.onion - Whispernote Одноразовые записки с шифрованием, есть возможность прицепить картинки, ставить пароль и количество вскрытий записки. Торговая площадка существует около двух лет, имеет легкий и удобный дизайн. Борды/Чаны. Onion - cryptex note  сервис одноразовых записок, уничтожаются после просмотра. Даркнет маркет запущен около года назад и в настоящее время насчитывает около 250 магазинов. Форум Форумы lwplxqzvmgu43uff. Onion - SleepWalker, автоматическая продажа различных виртуальных товаров, обменник (сомнительный ресурс, хотя кто знает). Onion - Sci-Hub  пиратский ресурс, который открыл массовый доступ к десяткам миллионов научных статей. Даркнет каталог сайтов не несет никакой ответственности за действия пользователей. (upd: ахтунг! Foggeddriztrcar2.onion - Bitcoin Fog  микс-сервис для очистки биткоинов, наиболее старый и проверенный, хотя кое-где попадаются отзывы, что это скам и очищенные биткоины так и не при приходят их владельцам. Разное/Интересное Тип сайта Адрес в сети TOR Краткое описание Биржи Биржа (коммерция) Ссылка удалена по притензии роскомнадзора Ссылка удалена по притензии роскомнадзора Ссылзии. Мы выступаем за свободу слова. Hbooruahi4zr2h73.onion - Hiddenbooru  Коллекция картинок по типу Danbooru. Наверняка, вам будет интересно узнать что же это такое и погрузить в эту тему глубже. Зеркало сайта z pekarmarkfovqvlm. Onion - Harry71  список существующих TOR-сайтов. Система рейтингов продавцов. Onion - Первая анонимная фриланс биржа  первая анонимная фриланс биржа weasylartw55noh2.onion - Weasyl  Галерея фурри-артов Еще сайты Тор ТУТ! Продажа «товаров» через даркнет сайты Такими самыми популярными товарами на даркнете считают личные данные (переписки, документы, пароли компромат на известнейших людей, запрещенные вещества, оружие, краденые вещи (чаще всего гаджеты и техника фальшивые деньги (причем обмануть могут именно вас). Статья 327 УК РФ лишение свободы на срок до двух лет. Что-то про аниме-картинки пок-пок-пок. Underdj5ziov3ic7.onion - UnderDir, модерируемый каталог ссылок с возможностью добавления. Безопасность Tor. Onion/ - Dream Market  европейская площадка по продаже, медикаментов, документов. TLS, шифрование паролей пользователей, 100 доступность и другие плюшки. Годный сайтик для новичков, активность присутствует. Просмотр.onion сайтов без браузера Tor(Proxy). Onion/ - Годнотаба  открытый сервис мониторинга годноты в сети TOR. В 2022 году все.onion сайты перешли на новые адреса версии. Playboyb2af45y45.onion - ничего общего с журнало м playboy journa. Представлен в виде десктопного приложения под операционные системы Windows, Linux и MacOS. Onion - RetroShare  свеженькие сборки ретрошары внутри тора strngbxhwyuu37a3.onion - SecureDrop  отправка файлов и записочек журналистам The New Yorker, ну мало ли yz7lpwfhhzcdyc5y.onion - Tor Project Onion  спи. Onion - Verified,.onion зеркало кардинг форума, стоимость регистрации. Onion - Probiv  достаточно популярный форум по пробиву информации, обсуждение и совершение сделок по различным серых схемам. Onion - TorGuerrillaMail  одноразовая почта, зеркало сайта m 344c6kbnjnljjzlz. Onion сайтов без браузера Tor ( Proxy ) Просмотр.onion сайтов без браузера Tor(Proxy) - Ссылки работают во всех браузерах. Форум отлично подойдет как новичкам в нашем бизнесе, так и специалистам высокого уровня. Простая система заказа и обмен моментальными сообщениями с Админами (после моментальной регистрации без подтверждения данных) valhallaxmn3fydu. 4.4/5 Ссылка TOR зеркало Ссылка Только TOR TOR зеркало omgomgnxqxpzc7m6kthcwr6cawayn2fhnbjww3lgcgvfpgb4xh55ovid.

Tor onion site

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.

Главная / Карта сайта

Не заходит на аккаунт крамп

Кракен официальный сайт krmp.cc onion

Ровная ссылка на kraken