Tracking User Application Activity by using Machine Learning Techniques on Network Traffic

A network eavesdropper may invade the privacy of an online user by collecting the passing traffic and classifying the applications that generated the network traffic. This collection may be used to build fingerprints of the user's Internet usage. In this paper, we investigate the feasibility of performing such breach on encrypted network traffic generated by actual users. We adopt the random forest algorithm to classify the applications in use...