SideChannel Information Leakage of Traffic Data in Instant Messaging Instant Messaging has been widely applied for both corporate use and personal use in recent years. Major Instant Messaging service providers adopt the Push Technology to ensure the immediacy of message forwarding, which efficiently provides a great convenience for user. However, the immediacy feature causes sidechannel information leakage even if some protection measures has been implemented, such as information encryption strategy. In particular, we observe that sendersxe2x80x99 traffic flows have a strong temporal correlation with those of corresponding recipients, since the messages are forwarded to recipients as soon as they are received by servers. Based on the observation, attackers can infer realtime communications between pairwise users and even the social connections of users. In this paper, we present a methodology framework to validate this sidechannel information leakage, which identifies users of realtime communications by matching the pairwise time sequences of traffic flows. We evaluate the method on the collected realworld data. The experimental results show that usersxe2x80x99 communications can be identified with a high accuracy, and 6 groups of users are inferred to have strong connections based on the data collected from a local area networks.

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.