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1. Use of Auxiliary Classifier Generative Adversarial Network in Touchstroke Authentication

   https://doi.org/10.1109/icmla51294.2020.00049  

   Deb, Debzani; Guirguis, Mina M. (December 2020, 19th IEEE International Conference on Machine Learning and Applications (ICMLA))

   null (Ed.)

   With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option, especially when the physical biometrics are challenging to utilize, or their frequent and continuous usage annoys the user. However, touch dynamics is vulnerable to potential security attacks such as shoulder surfing, camera attack, and smudge attack. As a result, it is challenging to rule out genuine imposters while only relying on models that learn from real touchstrokes. In this paper, a touchstroke authentication model based on Auxiliary Classifier Generative Adversarial Network (AC-GAN) is presented. Given a small subset of a legitimate user's touchstrokes data during training, the presented AC-GAN model learns to generate a vast amount of synthetic touchstrokes that closely approximate the real touchstrokes, simulating imposter behavior, and then uses both generated and real touchstrokes in discriminating real user from the imposters. The presented network is trained on the Touchanalytics dataset and the discriminability is evaluated with popular performance metrics and loss functions. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with Equal Error Rate ranging from 2% to 11% even when the generative model is challenged with a vast number of synthetic data that effectively simulates an imposter behavior. The use of AC-GAN also diversifies generated samples and stabilizes training. 

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2. Multi-Modality Mobile Datasets for Behavioral Biometrics Research: Data/Toolset paper

   https://doi.org/10.1145/3577923.3583637  

   Ray-Dowling, Aratrika; Wahab, Ahmed Anu; Hou, Daqing; Schuckers, Stephanie (April 2023, CODASPY '23: Proceedings of the Thirteenth ACM Conference on Data and Application Security and Privacy)

   The ubiquity of mobile devices nowadays necessitates securing the apps and user information stored therein. However, existing one-time entry-point authentication mechanisms and enhanced security mechanisms such as Multi-Factor Authentication (MFA) are prone to a wide vector of attacks. Furthermore, MFA also introduces friction to the user experience. Therefore, what is needed is continuous authentication that once passing the entry-point authentication, will protect the mobile devices on a continuous basis by confirming the legitimate owner of the device and locking out detected impostor activities. Hence, more research is needed on the dynamic methods of mobile security such as behavioral biometrics-based continuous authentication, which is cost-effective and passive as the data utilized to authenticate users are logged from the phone's sensors. However, currently, there are not many mobile authentication datasets to perform benchmarking research. In this work, we share two novel mobile datasets (Clarkson University (CU) Mobile datasets I and II) consisting of multi-modality behavioral biometrics data from 49 and 39 users respectively (88 users in total). Each of our datasets consists of modalities such as swipes, keystrokes, acceleration, gyroscope, and pattern-tracing strokes. These modalities are collected when users are filling out a registration form in sitting both as genuine and impostor users. To exhibit the usefulness of the datasets, we have performed initial experiments on selected individual modalities from the datasets as well as the fusion of simultaneously available modalities. 

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3. Biometrics-as-a-Service: A framework to promote innovative biometric recognition in the cloud

   Talreja, Veeru; Ferrett, Terry; Valenti, Mathew; Ross, Arun (January 2018, Proc. IEEE Int. Conf. on Consumer Electronics (ICCE))

   Biometric recognition, or simply biometrics, is the use of biological attributes such as face, fingerprints or iris in order to recognize an individual in an automated manner. A key application of biometrics is authentication; i.e., using said biological attributes to provide access by verifying the claimed identity of an individual. This paper presents a framework for Biometrics-as-a-Service (BaaS) that performs biometric matching operations in the cloud, while relying on simple and ubiquitous consumer devices such as smartphones. Further, the framework promotes innovation by providing interfaces for a plurality of software developers to upload their matching algorithms to the cloud. When a biometric authentication request is submitted, the system uses a criteria to automatically select an appropriate matching algorithm. Every time a particular algorithm is selected, the corresponding developer is rendered a micropayment. This creates an innovative and competitive ecosystem that benefits both software developers and the consumers. As a case study, we have implemented the following: (a) an ocular recognition system using a mobile web interface providing user access to a biometric authentication service, and (b) a Linux-based virtual machine environment used by software developers for algorithm development and submission. 

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4. Increasing Security of WebIDs Through Biometrics

   https://doi.org/10.1109/southeastcon44009.2020.9368274  

   Gwyn, Tony; Martin, Taylor; Esterline, Albert (March 2020, 2020 IEEE SoutheastCon)

   null (Ed.)

   We are creating a streamlined way to adapt WebIDs [1], and biometrics [2] to the cyber world. This involves building a user authentication system that enables quick, fast and secure access. It is understood that compared to traditional username and password user authentication, WebIDs are designed to provide such services. Nevertheless, if an intruder either has direct access to the user's computer or somehow gets the unique certificate of the user, important information can be stolen with solely the use of WebIDs. Since biometric data (e.g. fingerprints, iris scanning, etc.) is unique and not easily duplicated, this possibility can be avoided by including biometrics in the authentication process. We also include an enrollment protocol that checks whether a user has a WebID while trying to access a server. If they do, we allow the user access to the server, and if they do not, by accessing their own server, we register the user for a WebID with their permission. Implementing these features in the WebID protocol will greatly enhance user authentication safety. 

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5. Enhanced free-text keystroke continuous authentication based on dynamics of wrist motion

   https://doi.org/10.1109/WIFS.2017.8267642  

   Li, Borui; Sun, Han; Gao, Yang; Phoha, Vir V.; Jin, Zhanpeng (December 2017, IEEE Workshop on Information Forensics and Security (WIFS), Rennes, France)

   Free-text keystroke is a form of behavioral biometrics which has great potential for addressing the security limitations of conventional one-time authentication by continuously monitoring the user's typing behaviors. This paper presents a new, enhanced continuous authentication approach by incorporating the dynamics of both keystrokes and wrist motions. Based upon two sets of features (free-text keystroke latency features and statistical wrist motion patterns extracted from the wrist-worn smartwatches), two one-vs-all Random Forest Ensemble Classifiers (RFECs) are constructed and trained respectively. A Dynamic Trust Model (DTM) is then developed to fuse the two classifiers' decisions and realize non-time-blocked real-time authentication. In the free-text typing experiments involving 25 human subjects, an imposter/intruder can be detected within no more than one sentence (average 56 keystrokes) with an FRR of 1.82% and an FAR of 1.94%. Compared with the scheme relying on only keystroke latency which has an FRR of 4.66%, an FAR of 17.92% and the required number of keystroke of 162, the proposed authentication system shows significant improvements in terms of accuracy, efficiency, and usability. 

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