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1. A Survey on Heart Biometrics

   https://doi.org/10.1145/3410158  

   Rathore, Aditya Singh; Li, Zhengxiong; Zhu, Weijin; Jin, Zhanpeng; Xu, Wenyao (February 2021, ACM Computing Surveys)

   null (Ed.)

   In recent years, biometrics (e.g., fingerprint or face recognition) has replaced traditional passwords and PINs as a widely used method for user authentication, particularly in personal or mobile devices. Differing from state-of-the-art biometrics, heart biometrics offer the advantages of liveness detection, which provides strong tolerance to spoofing attacks. To date, several authentication methods primarily focusing on electrocardiogram (ECG) have demonstrated remarkable success; however, the degree of exploration with other cardiac signals is still limited. To this end, we discuss the challenges in various cardiac domains and propose future prospectives for developing effective heart biometrics systems in real-world applications. 

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2. 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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3. 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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4. MicPrint: acoustic sensor fingerprinting for spoof-resistant mobile device authentication

   https://doi.org/10.1145/3360774.3360801  

   Lee, Yongwoo; Li, Jingjie; Kim, Younghyun (November 2019, Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services)

   Smartphones are the most commonly used computing platform for accessing sensitive and important information placed on the Internet. Authenticating the smartphone's identity in addition to the user's identity is a widely adopted security augmentation method since conventional user authentication methods, such as password entry, often fail to provide strong protection by itself. In this paper, we propose a sensor-based device fingerprinting technique for identifying and authenticating individual mobile devices. Our technique, called MicPrint, exploits the unique characteristics of embedded microphones in mobile devices due to manufacturing variations in order to uniquely identify each device. Unlike conventional sensor-based device fingerprinting that are prone to spoofing attack via malware, MicPrint is fundamentally spoof-resistant since it uses acoustic features that are prominent only when the user blocks the microphone hole. This simple user intervention acts as implicit permission to fingerprint the sensor and can effectively prevent unauthorized fingerprinting using malware. We implement MicPrint on Google Pixel 1 and Samsung Nexus to evaluate the accuracy of device identification. We also evaluate its security against simple raw data attacks and sophisticated impersonation attacks. The results show that after several incremental training cycles under various environmental noises, MicPrint can achieve high accuracy and reliability for both smartphone models. 

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5. Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study

   https://doi.org/10.3390/fi13070164  

   Gwyn, Tony; Roy, Kaushik; Atay, Mustafa (July 2021, Future Internet)

   null (Ed.)

   In the realm of computer security, the username/password standard is becoming increasingly antiquated. Usage of the same username and password across various accounts can leave a user open to potential vulnerabilities. Authentication methods of the future need to maintain the ability to provide secure access without a reduction in speed. Facial recognition technologies are quickly becoming integral parts of user security, allowing for a secondary level of user authentication. Augmenting traditional username and password security with facial biometrics has already seen impressive results; however, studying these techniques is necessary to determine how effective these methods are within various parameters. A Convolutional Neural Network (CNN) is a powerful classification approach which is often used for image identification and verification. Quite recently, CNNs have shown great promise in the area of facial image recognition. The comparative study proposed in this paper offers an in-depth analysis of several state-of-the-art deep learning based-facial recognition technologies, to determine via accuracy and other metrics which of those are most effective. In our study, VGG-16 and VGG-19 showed the highest levels of image recognition accuracy, as well as F1-Score. The most favorable configurations of CNN should be documented as an effective way to potentially augment the current username/password standard by increasing the current method’s security with additional facial biometrics. 

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