Exploring capacitive swipe gesture for user authentication using a new large dataset - K.C., Kiran; Hossain, Shafaeat; Haberfeld, Carl (Computers & Security, 2025)
Keywords: Artificial intelligence; Authentication; Authentication (law); Biometrics; Capacitive sensing; Capacitive swipe gesture; Computer science; Computer security; Gesture; Operating system; Smartphone security; SwIPe; Touchscreen; dept.csc

Open code biometric tap pad for smartphones - Haberfeld, Carl; Hossain, Shafaeat; Lancor, Lisa (Journal of Information Security and Applications, 2021)
Poor security practices among smartphone users, such as the use of simple, easily guessed passcodes for logins, are a result of the effort required to memorize stronger ones. In this paper, we devise a concept of “open code” biometric tap pad to authenticate smartphone users, which eliminates the need of memorizing secret codes. A biometric tap pad consists of a grid of buttons each labeled with a unique digit. The user attempting to log into the phone will tap these buttons in a given sequence. He/she will not memorize this tap sequence. Instead, the sequence will be displayed on the screen. The focus here is how the user types the sequence. This typing behavior is used for authentication. An open code biometric tap pad has several advantages, such as (1) users do not need to memorize passcodes, (2) manufacturers do not need to include extra sensors, and (3) onlookers have no chance to practice shoulder-surfing. We designed three tap pads and incorporated them into an Android app. We evaluated the performance of these tap pads by experimenting with three sequence styles and five different fingers: two thumbs, two index fingers, and the “usual” finger. We collected data from 33 participants over two weeks. We tested three machine learning algorithms: Support Vector Machine, Artificial Neural Network, and Random Forest. Experimental results show significant promise of open code biometric tap pads as a solution to the problem of weak smartphone security practices used by a large segment of the population.
1 citations (Crossref) [2023-10-31]
Keywords: Authentication; Biometrics; Decision trees; Extra sensors; Fasteners; Index finger; Learning algorithms; Mobile security; Neural networks; Open codes; Secret code; Security Practice; Security systems; Shoulder surfing; Smartphone securities; Smartphones; Support vector machines; Typing behaviors; dept.csc; peer-reviewed

SAS Mobile Application for Diagnosis of Obstructive Sleep Apnea Utilizing Machine Learning Models - Haberfeld, Carl; Sheta, Alaa; Hossain, Md Shafaeat; Turabieh, Hamza; Surani, Salim (2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), 2020)
In this paper, we provide a consistent, inexpensive, and easy to use graphical user interface (GUI) smart phone application named Sleep Apnea Screener (SAS) that can diagnosis Obstructive Sleep Apnea (OSA) based on demographic data such as: gender, age, height, BMI, neck circumference, waist, etc., allowing a tentative diagnosis of OSA without the need for overnight tests. The developed smart phone application can diagnosis sleep apnea using a model trained with 620 samples collected from a sleep center in Corpus Christi, TX. Two machine learning classifiers (i.e., Logistic Regression (LR) and Support Vector Machine (SVM)) were used to diagnosis OSA. Our preliminary results show that at-home OSA screening is indeed possible, and that our application is effective method for covering large numbers of undiagnosed cases.
7 citations (Crossref) [2023-10-31]
Keywords: COVID-19; Corpus christi; Demographic data; Graphical user interfaces; Graphical user interfaces (GUI); Learning systems; Logistic regression; Logistics; Machine Learning; Machine learning; Machine learning models; Mathematical model; Medical services; Mobile applications; Mobile telecommunication systems; OSA; Obstructive sleep apnea; SVM; Sleep Apnea; Sleep apnea; Sleep research; Smart phones; Smart-phone applications; Smartphones; Support vector machines; Support vector regression; Two machines; Ubiquitous computing; dept.csc; pretesting