Smart Spying via Deep Learning: Inferring Your Activities from Encrypted Wireless Traffic Wireless networks nowadays are ubiquitous in our daily life. Due to the open channel nature, wireless networks are vulnerable to eavesdropping attacks. Though wireless conversation can be encrypted against eavesdropping, attackers can still infer a userxe2x80x99s activities via traffic analysis on encrypted data. Nevertheless, previous inference methods usually have the limitation that they can only achieve a relatively high accuracy in a specific domain. In this paper, we propose a smart spying strategy that can infer a userxe2x80x99s activities of multiple domains with a higher accuracy. We also develop a prototype tool on top of this strategy to conduct experiments. The evaluation results show our strategy works effectively in activity inference on encrypted data, with an accuracy rate as high as 99.17%.

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.