Two-Thumbs-Up: Physical protection for PIN entry secure against recording attacks

Nyang, DaeHun; Kim, Hyoungshick; Lee, Woojoo; Kang, Sung bae; Cho, Geumhwan; Lee, Mun Kyu; Mohaisen, Aziz

doi:10.1016/j.cose.2018.05.012

Cited by 28 publications

(11 citation statements)

References 23 publications

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“…5 Pressed area size by the user's fingers. 6 Error rate, which is the frequency of using backspaces or deletion option.…”

Section: Gait-based Authenticationmentioning

confidence: 99%

“…Knowledge-based approaches rely on the knowledge of the user; i.e., the user must provide certain information such as numeric password, PIN, graphical sequence, or a picture gesture [4], to access a device [5]. Despite their simplicity, ease of implementation, and user acceptance, such approaches suffer from several shortcomings such as the inconvenience of frequent re-entering (especially when the knowledge used are long enough to convey strong security) and several adversarial attacks (e.g., shoulder surfing and smudge attacks) [6]- [10]. Another issue with knowledgebased authentication is the underlying assumption of having equal security requirements for all applications [11].…”

Section: Introductionmentioning

confidence: 99%

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

Abuhamad¹,

Abusnaina²,

Nyang³

et al. 2020

Preprint

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Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and interact with sensitive and private information. The security and protection of such personal information are becoming more and more important since mobile devices are vulnerable to unauthorized access or theft. User authentication is a task of paramount importance that grants access to legitimate users at the point-of-entry and continuously through the usage session. This task is made possible with today's smartphones' embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits. In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motionbased methods (27 studies), gait-based methods (23 studies), keystroke dynamics-based methods (20 studies), touch gesturebased methods (29 studies), voice-based methods (16 studies), and multimodal-based methods (33 studies). 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.

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“…5 Pressed area size by the user's fingers. 6 Error rate, which is the frequency of using backspaces or deletion option.…”

Section: Gait-based Authenticationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Abuhamad¹,

Abusnaina²,

Nyang³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…More users are willing to store private information on their devices and use them to process some sensitive information for mobility and convenience [27,50]. However, this also makes smartphones a major target by cyber-criminals [31]. If attackers get the phone and unlock it successfully, then they can easily steal all sensitive data.…”

Section: Introductionmentioning

confidence: 99%

“…Up to now, the most widely adopted authentication approach is still based on textual passwords. For example, iPhones use PIN code to protect the devices, but it may suffer many invasions, e.g., recording attacks [31]. In real-world applications, users have multiple accounts and may choose easy-to-remember passwords due to the multiple password inference [28] and limitation of long term memory [49].…”

Section: Introductionmentioning

confidence: 99%

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SwipeVLock: A Supervised Unlocking Mechanism Based on Swipe Behavior on Smartphones

Tan²,

Meng

et al. 2019

Machine Learning for Cyber Security

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Smartphones have become a necessity in people's daily lives, and changed the way of communication at any time and place. Nowadays, mobile devices especially smartphones have to store and process a large amount of sensitive information, i.e., from personal to financial and professional data. For this reason, there is an increasing need to protect the devices from unauthorized access. In comparison with the traditional textual password, behavioral authentication can verify current users in a continuous way, which can complement the existing authentication mechanisms. With the advanced capability provided by current smartphones, users can perform various touch actions to interact with their devices. In this work, we focus on swipe behavior and aim to design a machine learning-based unlock scheme called SwipeVLock, which verifies users based on their way of swiping the phone screen with a background image. In the evaluation, we measure several typical supervised learning algorithms and conduct a user study with 30 participants. Our experimental results indicate that participants could perform well with SwipeVLock, i.e., with a success rate of 98% in the best case.

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Designing Double-Click-Based Unlocking Mechanism on Smartphones

Tan

Zhu

et al. 2021

Security, Privacy, and Anonymity in Computation, Communication, and Storage

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Two-Thumbs-Up: Physical protection for PIN entry secure against recording attacks

Cited by 28 publications

References 23 publications

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

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

SwipeVLock: A Supervised Unlocking Mechanism Based on Swipe Behavior on Smartphones

Designing Double-Click-Based Unlocking Mechanism on Smartphones

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