2023/09/07 by Jingyang Hu, H. Wang, Hongbo Wang +5 · 3 voices · 1 citation
Computer Science · Engineering · #Indoor and Outdoor Localization Technologies #Internet Traffic Analysis and Secure E-voting #Wireless Communication Security Techniques #cs.CR
paper · pdf · doi:10.1145/3576915.3623088
arxiv published 2023/09/07 · arxiv updated 2023/09/07 · openalex created_date 2023/09/09 · openalex publication_date 2023/11/15 · openalex updated_date 2026/08/01
The contact-free sensing nature of Wi-Fi has been leveraged to achieve privacy breaches, yet existing attacks relying on Wi-Fi CSI (channel state information) demand hacking Wi-Fi hardware to obtain desired CSIs. Since such hacking has proven prohibitively hard due to compact hardware, its feasibility in keeping up with fast-developing Wi-Fi technology becomes very questionable. To this end, we propose WiKI-Eve to eavesdrop keystrokes on smartphones without the need for hacking. WiKI-Eve exploits a new feature, BFI (beamforming feedback information), offered by latest Wi-Fi hardware: since BFI is transmitted from a smartphone to an AP in clear-text, it can be overheard (hence eavesdropped) by any other Wi-Fi devices switching to monitor mode. As existing keystroke inference methods offer very limited generalizability, WiKI-Eve further innovates in an adversarial learning scheme to enable its inference generalizable towards unseen scenarios. We implement WiKI-Eve and conduct extensive evaluation on it; the results demonstrate that WiKI-Eve achieves 88.9% inference accuracy for individual keystrokes and up to 65.8% top-10 accuracy for stealing passwords of mobile applications (e.g., WeChat).