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Journal ArticleDOI

ShakeIn: Secure User Authentication of Smartphones with Single-Handed Shakes

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TLDR
ShakeIn is proposed, a handy user authentication scheme for secure unlocking of a smartphone by simply shaking the phone, with embedded motion sensors that can effectively capture the unique and reliable biometrical features of users about how they shake.
Abstract
Smartphones have been widely used with a vast array of sensitive and private information stored on these devices. To secure such information from being leaked, user authentication schemes are necessary. Current password/pattern-based user authentication schemes are vulnerable to shoulder surfing attacks and smudge attacks. In contrast, stroke/gait-based schemes are secure but inconvenient for users to input. In this paper, we propose ShakeIn, a handy user authentication scheme for secure unlocking of a smartphone by simply shaking the phone. With embedded motion sensors, ShakeIn can effectively capture the unique and reliable biometrical features of users about how they shake. In this way, even if an attacker sees a user shaking his/her phone, the attacker can hardly reproduce the same behavior. Furthermore, by allowing users to customize the way they shake the phone, ShakeIn endows users with the maximum operation flexibility. We implement ShakeIn and conduct both intensive trace-driven simulations and real experiments on 20 volunteers with about 530,555 shaking samples collected over multiple months. The results show that ShakeIn achieves an average equal error rate of 1.2 percent with a small number of shakes using only 35 training samples even in the presence of shoulder-surfing attacks.

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Citations
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Journal ArticleDOI

Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing

TL;DR: A novel continuous authentication scheme is proposed for smartphone users, which is based on activity pattern recognition, which recognizes smartphone users on the basis of their physical activity patterns using accelerometer, gyroscope, and magnetometer sensors of smartphone.
Journal ArticleDOI

AUTo Sen : Deep-Learning-Based Implicit Continuous Authentication Using Smartphone Sensors

TL;DR: AUToSen is a deep-learning-based active authentication approach that exploits sensors in consumer-grade smartphones to authenticate a user based on deep learning to identify user distinct behavior from the embedded sensors with and without the user’s interaction with the smartphone.
Journal ArticleDOI

Sensor-Based Continuous Authentication of Smartphones’ Users Using Behavioral Biometrics: A Contemporary Survey

TL;DR: The survey provides an overview of the current state-of-the-art approaches for continuous user authentication using behavioral biometrics captured by smartphones’ embedded sensors, including insights and open challenges for adoption, usability, and performance.
Journal ArticleDOI

PROTECT: Efficient Password-Based Threshold Single-Sign-On Authentication for Mobile Users against Perpetual Leakage

TL;DR: A password-based threshold single-sign-on authentication scheme dubbed PROTECT is proposed that thwarts adversaries who can compromise identity server(s), where multiple identity servers are introduced to authenticate mobile users and issue authentication tokens in a threshold way and proves that it can be easily deployed on mobile devices.
Posted Content

Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Contemporary Survey

TL;DR: In this paper, the authors survey more than 140 behavioral biometric-based approaches for continuous user authentication, including motion-based methods (28 studies), gait-based techniques (19 studies), keystroke dynamics-based models (20 studies), touch gesture-based method (29 studies), voice-based model (16 studies), and multimodal-based approach (34 studies).
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