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1. Inverting Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models

   https://doi.org/10.1109/IJCB54206.2022.10007997  

   Ahmad, Sohaib ; Mahmood, Kaleel ; Fuller, Benjamin ( October 2022 , 2022 IEEE Interational Joint Conference on Biometrics)

   Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a prime candidate for this type of attack. This is because inverting a biometric model allows the attacker to produce a realistic biometric input to spoof biometric authentication systems. One of the main constraints in conducting a successful model inversion attack is the amount of training data required. In this work, we focus on iris and facial biometric systems and propose a new technique that drastically reduces the amount of training data necessary. By leveraging the output of multiple models, we are able to conduct model inversion attacks with 1/10th the training set size of Ahmad and Fuller (IJCB 2020) for iris data and 1/1000th the training set size of Mai et al. (Pattern Analysis and Machine Intelligence 2019) for facial data. We denote our new attack technique as structured random with alignment loss. 

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2. Mouse Dynamics Behavioral Biometrics: A Survey

   https://doi.org/10.1145/3640311  

   Khan, Simon ; Devlen, Charles ; Manno, Michael ; Hou, Daqing ( June 2024 , ACM Computing Surveys)

   Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are expected to be low cost, unobtrusive, and ideally ubiquitous in nature. Behavioral biometric modalities such as mouse dynamics (mouse behaviors on a graphical user interface (GUI)) and widget interactions (another modality closely related to mouse dynamics that also considers the target (widget) of a GUI interaction, such as links, buttons, and combo-boxes) can bolster the security of existing authentication systems because of their ability to distinguish individuals based on their unique features. As a result, it can be difficult for an imposter to impersonate these behavioral biometrics, making them suitable for authentication. In this article, we survey the literature on mouse dynamics and widget interactions dated from 1897 to 2023. We begin our survey with an account of the psychological perspectives on behavioral biometrics. We then analyze the literature along the following dimensions: tasks and experimental settings for data collection, taxonomy of raw attributes, feature extractions and mathematical definitions, publicly available datasets, algorithms (statistical, machine learning, and deep learning), data fusion, performance, and limitations. We end the paper with presenting challenges and promising research opportunities.

    

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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. Continuous authentication using one-class classifiers and their fusion

   https://doi.org/10.1109/ISBA.2018.8311467  

   Kumar, Rajesh ; Kundu, Partha Pratim ; Phoha, Vir V. ( January 2018 , IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA))

   While developing continuous authentication systems (CAS), we generally assume that samples from both genuine and impostor classes are readily available. However, the assumption may not be true in certain circumstances. Therefore, we explore the possibility of implementing CAS using only genuine samples. Specifically, we investigate the usefulness of four one-class classifiers OCC (elliptic envelope, isolation forest, local outliers factor, and one-class support vector machines) and their fusion. The performance of these classifiers was evaluated on four distinct behavioral biometric datasets, and compared with eight multi-class classifiers (MCC). The results demonstrate that if we have sufficient training data from the genuine user the OCC, and their fusion can closely match the performance of the majority of MCC. Our findings encourage the research community to use OCC in order to build CAS as it does not require knowledge of impostor class during the enrollment process. 

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5. Analysis of head and torso movements for authentication

   https://doi.org/10.1109/ISBA.2018.83114  

   Parimi, GM ; Kundu, PP ; Phoha, VV ( January 2018 , IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA))

   Wearable computing devices have become increasingly popular and while these devices promise to improve our lives, they come with new challenges. One such device is the Google Glass from which data can be stolen easily as the touch gestures can be intercepted from a head-mounted device. This paper focuses on analyzing and combining two behavioral metrics, namely, head movement (captured through glass) and torso movement (captured through smartphone) to build a continuous authentication system that can be used on Google Glass alone or by pairing it with a smartphone. We performed a correlation analysis among the features on these two metrics and found that very little correlation exists between the features extracted from head and torso movements in most scenarios (set of activities). This led us to combine the two metrics to perform authentication. We built an authentication system using these metrics and compared the performance among different scenarios. We got EER less than 6% when authenticating a user using only the head movements in one scenario whereas the EER is less than 5% when authenticating a user using both head and torso movements in general. 

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