UltraPIN: Inferring PIN Entries via Ultrasound

Xi-ming, Liu; Li, Yingjiu; Deng, Robert H.

doi:10.1145/3433210.3453075

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…Arp et al [49] explored the functionality, prevalence, and technical limitations of ultrasonic beacons embedded in audio and tracked users using mobile device microphones. PatternListener [50] and UltraPIN [51] utilized the user's smartphone to emit ultrasonic signals to track the user's finger movement and infer the unlock pattern and PIN respectively. PrintListener is the first work to infer users' fingerprint information using acoustic signals.…”

Section: B User Information Inferring Via Acousticmentioning

confidence: 99%

PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

Zhou,

Su,

Wang

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS), called PrintListener, which leverages users' fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users' fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in realworld scenarios show that Printlistener can significantly improve the attack potency of MasterPrint.

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Section: B User Information Inferring Via Acousticmentioning

confidence: 99%

PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

Zhou,

Su,

Wang

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

View full text Add to dashboard Cite

show abstract

“…In [35], the sounds produced while typing the PIN have been used to infer the PIN secret combination. In [36], smartphone speakers have been used to produce ultrasound signals. Then, the PIN has been inferred based on the echos recorded by the smartphone microphone.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning for PIN Side-Channel Attacks Based on Smartphone Motion Sensors

2023

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Motion sensors are integrated into all mobile devices, providing information useful for several purposes. However, these sensor data can be read by any application and by websites accessed through a browser, without requiring security permissions. In this paper, we show that information about smartphone movements can lead to the identification of a Personal Identification Number (PIN) tapped by the user. To reduce the amount of sniffed data, we use an event-driven approach, where motion sensors are sampled only when a key is pressed. The acquired data are used to train a machine learning algorithm for the classification of the keystrokes in a supervised manner. We also consider that users insert the same PIN each time authentication is required, leading to further side-channel information available to the attacker. Numerical results show the feasibility of cyber-attacks based on motion sensors. For example, 4-digit PINs are correctly recognized at the first attempt with an accuracy of 37%, and in five attempts with an accuracy of 63%.

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UltraPIN: Inferring PIN Entries via Ultrasound

Cited by 2 publications

References 29 publications

PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

Machine Learning for PIN Side-Channel Attacks Based on Smartphone Motion Sensors

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