lopadult.blogg.se - Attack tor network

We collect a dataset comprised of more than three million network traces, which is the largest dataset of web traffic ever used for website fingerprinting, and find that the performance achieved by our deep learning approaches is comparable to known methods which include various research efforts spanning over multiple years. In this paper, we show that an adversary can automate the feature engineering process, and thus automatically deanonymize Tor traffic by applying our novel method based on deep learning. Typically, these features are manually engineered and, as such, any change introduced to the Tor network can render these carefully constructed features ineffective. The success of such attacks heavily depends on the particular set of traffic features that are used to construct the fingerprint. By capturing traffic traces between users and their Tor entry guard, a network eavesdropper can leverage this meta-data to reveal which website Tor users are visiting. Several studies have shown that the network traffic that is generated by a visit to a website over Tor reveals information specific to the website through the timing and sizes of network packets. These findings highlight the need for effective defenses that defeat deep-learning attacks and that could be deployed in Tor. Against traffic defended with WTF-PAD in this setting, the attack still can get 0.95 precision and 0.70 recall. In a realistic open-world setting, our attack remains effective, with 0.99 precision and 0.94 recall on undefended traffic. Walkie-Talkie remains effective, holding the attack to just 49.7% accuracy. The CNN attack attains over 98% accuracy on Tor traffic without defenses, better than all prior attacks, and it is also effective against WTF-PAD with over 90% accuracy. In this work, we propose a new website fingerprinting attack against Tor that leverages a type of deep learning called convolution neural networks (CNN), and we evaluate this attack against WTF-PAD and Walkie-Talkie. Recently, lightweight website fingerprinting defenses for Tor have been proposed that substantially degrade existing attacks: WTF-PAD and Walkie-Talkie. State-of-the-art website fingerprinting attacks have been shown to be effective even against Tor. Website fingerprinting enables a local eavesdropper to determine which websites a user is visiting over an encrypted connection.

Next, there are comparison of the surveyed traffic classification and discussion on their classification properties. There is preliminary discussion on machine learning approaches and Tor network. This paper presents survey on existing approaches for classification of Tor and other encrypted traffic. Therefore, numerous of research has been performed on encrypted traffic analyzing and classification using machine learning techniques. However, the strong anonymity also became the heaven for criminal to avoid network tracing. It also implements strong defense to protect the users against traffic features extraction and website fingerprinting. Tor protect its user privacy against surveillance and censorship using strong encryption and obfuscation techniques which makes it extremely difficult to monitor and identify users' activity on the Tor network. Tor (The Onion Router) is an anonymity tool that is widely used worldwide.