Deep Learning Enabled Keystroke Eavesdropping Attack Over Videoconferencing Platforms

Xueyi Wang, Yifan Liu, Shancang Li

Abstract: The COVID-19 pandemic has significantly impacted people by driving people to work from home using communication tools such as Zoom, Teams, Slack, etc. The users of these communication services have exponentially increased in the past two years, e.g., Teams annual users reached 270 million in 2022 and Zoom averaged 300 million daily active users in videoconferencing platforms. However, using edging artificial intelligence techniques, new cyber attacking tools expose these services to eavesdropping or disruption. This work investigates keystroke eavesdropping attacks on physical keyboards using deep learning techniques to analyze the acoustic emanation of keystroke audios to identify victims' keystrokes. An accurate context-free inferring algorithm was developed that can automatically predict keystrokes during inputs. The experimental results demonstrated that the accuracy of keystroke inference approaches is around 90% over normal laptop keyboards.