Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning

2024/03/06 by Nascimento, Pedro Gomes do, Witiak, Pidge, MacCallum, Tucker +2
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2403.03832

Abstract

This research aims to further understanding in the field of continuous authentication using behavioral biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch dynamics can effectively distinguish users. However, further studies are needed to make it viable option for authentication systems

Related

• Continuous User Authentication Using Machine Learning and Multi-Finger Mobile Touch Dynamics with a Novel Dataset
• Your Identity is Your Behavior -- Continuous User Authentication based on Machine Learning and Touch Dynamics
• Hold On and Swipe: A Touch-Movement Based Continuous Authentication Schema based on Machine Learning
• Machine Learning Algorithms In User Authentication Schemes
• Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning