Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap with arXiv:1703.0352

Mobiles and wearables: owner biometrics and authentication

We discuss the design and development of HCI models for authentication based on gait and gesture that can be supported by mobile and wearable equipment. The paper proposes to use such biometric behavioral traits for partially transparent and continuous authentication by means of behavioral patterns. © 2016 Copyright held by the owner/author(s)