Behavicker: Eavesdropping Computer-Usage Activities through Acoustic Side Channel

Abstract

Computers are widely used for business and entertainment purposes throughout our modern lives. Computer kits provide a variety of services such as text processing, programming, shopping, and gaming. Computers have greatly enhanced the quality of our lives; however, we discover an often-overlooked fact that engaging in computer-related activities may be eavesdropped upon by an attacker by sniffing the emitted acoustic signals from keyboard and mouse. The activity of eavesdropping via acoustic side channel has lower requirements in terms of hardware instrumentation and is easier to implement in real-world applications than other side channel attacks that have been presented in previous work. In this paper, we design and implement a system, namely, Behavicker, to validate the feasibility of this kind of attack. Unlike conventional activity recognition, Behavicker infers high-level computer-usage activities with a semantics-preserving multiscale learning scheme, based on the recognition of basic keyboard and mouse events including left click, right click, middle click, scrolling up, and scrolling down. Real-world experiments show that Behavicker can recognize six interaction events with an accuracy of 88.3% and infer computer-usage activities with an accuracy of 82.7% in an indoor environment.