AUTo Sen : Deep-Learning-Based Implicit Continuous Authentication Using Smartphone Sensors

摘要: Smartphones have become crucial for our daily life activities and are increasingly loaded with personal information to perform several sensitive tasks, including, mobile banking communication, used storing private photos files. Therefore, there is a high demand applying usable authentication techniques that prevent unauthorized access information. In this article, we propose AUTo Sen , deep-learning-based active approach exploits sensors in consumer-grade smartphones authenticate user. Unlike conventional approaches, based on deep learning identify user distinct behavior from the embedded without user’s interaction smartphone. We investigate different architectures modeling capturing users’ behavioral patterns purpose of authentication. Moreover, explore sufficiency sensory data required accurately users. evaluate real-world set includes 84 participants’ collected using designed data-collection application. The experiments show operates readings only three (accelerometer, gyroscope, magnetometer) frequency, e.g., one attempt every 0.5 s. Using second enables an F1-score approximately 98%, false acceptance rate (FAR) 0.95%, rejection (FRR) 6.67%, equal error (EER) 0.41%. While half 97.52%, FAR 0.96%, FRR 8.08%, EER 0.09%. effects at variable sampling periods performance models under various settings architectures.