Inferring users' online activities through traffic analysis

Traffic analysis may threaten user privacy, even if the traffic is encrypted. In this paper, we use IEEE 802.11 wireless local area networks (WLANs) as an example to show that inferring users' online activities accurately by traffic analysis without the administrator's privilege is possible during very short periods (e.g., a few seconds). The online activities we investigated include web browsing, chatting, online gaming, downloading, uploading and video watching, etc. We implement a hierarchical classification system based on machine learning algorithms to discover what a user is doing on his/her computer. Furthermore, we conduct experiments in different network environments (e.g., at home, on university campus, and in public areas) with different application scenarios to evaluate the performance of the classification system. Results show that our system can distinguish different online applications on the accuracy of about 80% in 5 seconds and over 90% accuracy if the eavesdropping lasts for 1 minute.