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

Security and privacy aspects of mobile applications for post-surgical care

Mobile technologies have the potential to improve patient monitoring, medical decision making and in general the efficiency and quality of health delivery. They also pose new security and privacy challenges. The objectives of this work are to (i) Explore and define security and privacy requirements on the example of a post-surgical care application, and (ii) Develop and test a pilot implementation Post-Surgical Care Studies of surgical out- comes indicate that timely treatment of the most common complications in compliance with established post-surgical regiments greatly improve success rates. The goal of our pilot application is to enable physician to optimally synthesize and apply patient directed best medical practices to prevent post-operative complications in an individualized patient/procedure specific fashion. We propose a framework for a secure protocol to enable doctors to check most common complications for their patient during in-hospital post- surgical care. We also implemented our construction and cryptographic protocols as an iPhone application on the iOS using existing cryptographic services and libraries

A Forensically Sound Adversary Model for Mobile Devices

In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon forensic practitioners is that of forensic soundness. Our adversary model specifically considers and integrates the constraints of forensic soundness on the adversary, in our case, a forensic practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of forensic interest in both the external and internal storage of the mobile device