无需黑客攻击的密码窃取：Wi-Fi使能的实用按键窃听
Password-Stealing without Hacking: Wi-Fi Enabled Practical Keystroke Eavesdropping
AI总结：
提出WiKI-Eve，利用最新Wi-Fi硬件的BFI特性在无需黑客攻击的情况下窃听智能手机按键，并通过对抗学习提升泛化能力，实现88.9%的按键推断准确率和最高65.8%的密码窃取top-10准确率。
摘要：
Wi-Fi的非接触式感知特性已被利用来实现隐私泄露，然而现有的依赖Wi-Fi CSI（信道状态信息）的攻击需要黑客攻击Wi-Fi硬件以获取所需的CSI。由于紧凑的硬件设计，这种黑客攻击已被证明极其困难，其在跟上快速发展的Wi-Fi技术方面的可行性变得非常令人质疑。为此，我们提出了WiKI-Eve，用于在无需黑客攻击的情况下窃听智能手机上的按键。WiKI-Eve利用了最新Wi-Fi硬件提供的一项新特性——BFI（波束成形反馈信息）：由于BFI以明文形式从智能手机传输到AP（接入点），任何切换到监听模式的其他Wi-Fi设备都可以窃听到它（从而实现窃听）。由于现有的按键推断方法的泛化能力非常有限，WiKI-Eve进一步创新了一种对抗学习方案，使其推断能够泛化到未见过的场景。我们实现了WiKI-Eve并对其进行了广泛评估；结果表明，WiKI-Eve对单个按键实现了88.9%的推断准确率，并且在窃取移动应用（例如WeChat）密码时的top-10准确率高达65.8%。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
