Smart Spying via Deep Learning: Inferring Your Activities from Encrypted Wireless Traffic
Tao Hou, Tao Wang, Zhuo Lu, Yao Liu · 2019
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 user's 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 user's 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%.