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1. Understanding Attacking Behaviors Toward Password-based Mobile User Authentication

   Lina Zhou, Kanlun Wang ( July 2021 , Who Are You?! Adventures in Authentication Workshop)

   Password-based mobile user authentication is vulnerable to a variety of security threats. Shoulder-surfing is the key to those security threats. Despite a large body of research on password security with mobile devices, existing studies have focused on shaping the security behavior of mobile users by enhancing the strengths of user passwords or by establishing secure password composition policies. There is little understanding of how an attacker actually goes about observing the password of a target user. This study empirically examines attackers’ behaviors in observing passwordbased mobile user authentication sessions across the three observation attempts. It collects data through a longitudinal user study and analyzes the data collected through a system log. The results reveal several behavioral patterns of attackers. The findings suggest that attackers are strategic in deploying attacks of shoulder-surfing. The findings have implications for enhancing users’ password security and refining organizations’ password composition policies. 

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2. Shoulder Surfing on Mobile Authentication: Perception vis-a-vis Performance from the Attacker's Perspective

   https://doi.org/10.1109/ISI58743.2023.10297219  

   Wang, Kanlun ; Zhou, Lina ; Zhang, Dongsong ; Lai, Jianwei ( October 2023 , IEEE)

   Shoulder-surfing studies in the context of mobile user authentication have focused on evaluating the attackers' performance, yet have paid much less attention to their perception of the shoulder-surfing process. Whether and how the shoulder-surfing setting might affect the attackers' perception remains under-explored. This study aims to investigate the perception of shoulder surfers with two different password-based mobile user authentication methods and three different observation angles. Moreover, this work examines the relationship between the attackers' perception and performance in shoulder surfing and the possible moderating effect of the authentication method for the first time. Based on the data collected from an online experiment, our analysis results reveal the effects of authentication methods and observation angles on the attackers' perception in terms of cognitive workload, observation clarity, and repetitive learning advantage. In addition, the results also show that the relationship between the attackers' cognitive workload and performance in shoulder surfing varies with the mobile user authentication method. Our findings not only deepen the understanding of shoulder-surfing attacks from an attacker's perspective, but also facilitate developing countermeasures for shoulder-surfing attacks. 

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3. Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects [Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects]

   https://doi.org/10.5220/0010225804240431  

   Ray, Aratrika ; Hou, Daqing ; Schuckers, Stephanie ; Barbir, Abbie ( January 2021 , 7th International Conference on Information Systems Security and Privacy)

   Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as a password, PIN, fingerprint, iris, or face. But these authentication types are prone to a wide attack vector and worse 1 INTRODUCTION Currently smartphones are predominantly protected a patterned password is prone to smudge attacks, and fingerprint scanning is prone to spoof attacks. Other forms of attacks include video capture and shoulder surfing. Given the increasingly important roles smartphones play in e-commerce and other operations where security is crucial, there lies a strong need of continuous authentication mechanisms to complement and enhance one-time authentication such that even if the authentication at the point of login gets compromised, the device is still unobtrusively protected by additional security measures in a continuous fashion. The research community has investigated several continuous authentication mechanisms based on unique human behavioral traits, including typing, swiping, and gait. To this end, we focus on investigating physiological traits. While interacting with hand-held devices, individuals strive to achieve stability and precision. This is because a certain degree of stability is required in order to manipulate and interact successfully with smartphones, while precision is needed for tasks such as touching or tapping a small target on the touch screen (Sitov´a et al., 2015). As a result, to achieve stability and precision, individuals tend to develop their own postural preferences, such as holding a phone with one or both hands, supporting hands on the sides of upper torso and interacting, keeping the phone on the table and typing with the preferred finger, setting the phone on knees while sitting crosslegged and typing, supporting both elbows on chair handles and typing. On the other hand, physiological traits, such as hand-size, grip strength, muscles, age, 424 Ray, A., Hou, D., Schuckers, S. and Barbir, A. Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects. DOI: 10.5220/0010225804240431 In Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP 2021), pages 424-431 ISBN: 978-989-758-491-6 Copyrightc 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved still, once compromised, fail to protect the user’s account and data. In contrast, continuous authentication, based on traits of human behavior, can offer additional security measures in the device to authenticate against unauthorized users, even after the entry-point and one-time authentication has been compromised. To this end, we have collected a new data-set of multiple behavioral biometric modalities (49 users) when a user fills out an account recovery form in sitting using an Android app. These include motion events (acceleration and angular velocity), touch and swipe events, keystrokes, and pattern tracing. In this paper, we focus on authentication based on motion events by evaluating a set of score level fusion techniques to authenticate users based on the acceleration and angular velocity data. The best EERs of 2.4% and 6.9% for intra- and inter-session respectively, are achieved by fusing acceleration and angular velocity using Nandakumar et al.’s likelihood ratio (LR) based score fusion. 

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4. Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

   https://doi.org/10.3390/app10020541  

   Li, Qingqing ; Dong, Penghui ; Zheng, Jun ( January 2020 , Applied Sciences)

   Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F 1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock. 

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5. CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

   https://doi.org/10.1145/3307334.3326093  

   Liu, Jian ; Shi, Cong ; Chen, Yingying ; Liu, Hongbo ; Gruteser, Marco ( January 2019 , Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '19))

   With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over $99%$ average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%. 

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