Wireless Training-Free Keystroke Inference Attack and Defense

Yang, Edwin; Fang, Song; Markwood, Ian; Liu, Yao; Zhao, Shangqing; Lu, Zhuo; Zhu, Haojin

doi:10.1109/tnet.2022.3147721

Cited by 6 publications

(15 citation statements)

References 40 publications

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“…After connecting to the AP for accessing the Internet, Bob happens to have the need to access a sensitive account (e.g., online payment) protected by a password, which makes him a target of attack launched by Eve (see Figure 1). We follow the convention [34,67] to mainly focus on numerical passwords, but we also consider an extension to general KI in Section 5.4.2. From here on, our method diverges from existing ones that either demand a rogue AP to trick Bob into using its service [19,34], or require setting up extra Wi-Fi communication links to "sense" Bob's typing [2,67].…”

Section: Attack Scenarios and Methodsmentioning

confidence: 99%

“…We follow the convention [34,67] to mainly focus on numerical passwords, but we also consider an extension to general KI in Section 5.4.2. From here on, our method diverges from existing ones that either demand a rogue AP to trick Bob into using its service [19,34], or require setting up extra Wi-Fi communication links to "sense" Bob's typing [2,67]. Essentially, WiKI-Eve's attack method allows Eve to launch an attack on Bob regardless of which AP Bob is connected to.…”

Section: Attack Scenarios and Methodsmentioning

confidence: 99%

“…Among all side-channels, Wi-Fi CSI (channel state information) stands out as it appears to be void of all aforementioned weaknesses [34,67]. Essentially, since keystrokes affect wireless channels as shown in Figure 1, the "twisted" CSIs can be used to infer individual keys involved in typing a password.…”

Section: Introductionmentioning

confidence: 99%

“…However, we still face two challenges for realizing this idea. On one hand, passwords lack linguistic structure in natural languages (e.g., word structure and occurrence frequency of letters) to serve as prior information and features; this has forced existing password inference methods to either rely on independent keystroke features [34] or leverage transition features between two keystrokes [67]. Nonetheless, as these features have strong environment dependency, the resulting inference methods can hardly be generalized to unseen scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Password-Stealing without Hacking: Wi-Fi Enabled Practical Keystroke Eavesdropping

Hu,

Wang,

Zheng

et al. 2023

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

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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).

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Section: Attack Scenarios and Methodsmentioning

confidence: 99%

Section: Attack Scenarios and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Password-Stealing without Hacking: Wi-Fi Enabled Practical Keystroke Eavesdropping

Hu,

Wang,

Zheng

et al. 2023

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security

View full text Add to dashboard Cite

show abstract

“…Ref. [ 13 ] demonstrated that inference attack on physical keyboards using acoustic signals is possible on smartwatch, and [ 14 ] presents a keylogging inference attack typed with in-air tapping keyboard. These two studies do not install malicious codes to steal keyboard data without infecting target computers.…”

Section: Introductionmentioning

confidence: 99%

Vulnerability Analysis and Security Assessment of Secure Keyboard Software to Prevent PS/2 Interface Keyboard Sniffing

Lee

Yim

2023

Sensors

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Online security threats have arisen through Internet banking hacking cases, and highly sensitive user information such as the ID, password, account number, and account password that is used for online payments has become vulnerable. Many security companies have therefore researched protection methods regarding keyboard-entered data for the introduction of defense techniques. Recently, keyboard security issues have arisen due to the production of new malicious codes by attackers who have combined the existing attack techniques with new attack techniques; however, a keyboard security assessment is insufficient here. The research motivation is to serve more secure user authentication methods by evaluating the security of information input from the keyboard device for the user authentication, including Internet banking service. If the authentication information input from the keyboard device is exposed during user authentication, attackers can attempt to illegal login or, worst, steal the victim’s money. Accordingly, in this paper, the existing and the new keyboard-attack techniques that are known are surveyed, and the results are used as the basis for the implementation of sample malicious codes to verify both a security analysis and an assessment of secure keyboard software. As a result of the experiment, if the resend command utilization attack technique is used, 7 out of 10 companies’ products expose keyboard information, and only 1 company’s products detect it. The fundamental reason for these vulnerabilities is that the hardware chip related to the PS/2 interface keyboard does not provide security facilities. Therefore, since keyboard data exposure does not be prevented only by software, it is required to develop a hardware chip that provides security facilities.

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