Investigating the Streaming Algorithms Usage in Website Fingerprinting Attack Against Tor Privacy Enhancing Technology Website fingerprinting attack is a kind of traffic analysis attack that aims to identify the URL of visited websites using the Tor browser. Previous website fingerprinting attacks were based on batch learning methods which assumed that the traffic traces of each website are independent and generated from the stationary probability distribution. But, in realistic scenarios, the websitesu0027 concepts can change over time (dynamic websites) that is known as concept drift. To deal with data whose distribution change over time, the classifier model must update its model permanently and be adaptive to concept drift. Streaming algorithms are dynamic models that have these features and lead us to make a comparison of various representative data stream classification algorithms for website fingerprinting. Given to our experiments and results, by considering streaming algorithms along with statistical flowbased network traffic features, the accuracy grows significantly.

Integrating Machine Learning with OfftheShelf Traffic Flow Features for HTTPHTTPS Traffic Classification Accurate traffic classification is a key requirement for different network and security monitoringplanning tools. The evolution of Internet protocols and applications has caused traditional traffic classification approaches to be ineffective in certain cases. Key causes of the inaccuracy include: (i) the increase in the encrypted traffic; (ii) the rise in the usage of dynamic port numbers for different applications; and (iii) multiple applications running over HTTPHTTPS protocols. Traditional solutions for traffic analysis, classification, and measurement fall short in providing visibility in usersxe2x80x99 activities  a key requirement for network and security monitoring tools. In this paper, we evaluate an automatic classifier for encrypted Social media, Video and Audio traffic without relying on particular application layer header fields that can be easily modified. We leverage machine learning algorithms together with the features provided by the wellknown offtheshelf traffic flow exporters. We evaluate the performance of such a system also for generalization (robustness) purposes on different networks. Experimental results show promising performances in terms of generating robust traffic classification on large traffic data when the trained model is moved to different networks.