Effective Identity Management on Mobile Devices Using Multi-Sensor Measurements

Due to the dramatic increase in popularity of mobile devices in the past decade, sensitive user information is stored and accessed on these devices every day. Securing sensitive data stored and accessed from mobile devices, makes user-identity management a problem of paramount importance. The tension between security and usability renders the task of user-identity verification on mobile devices challenging. Meanwhile, an appropriate identity management approach is missing since most existing technologies for user-identity verification are either one-shot user verification or only work in restricted controlled environments. To solve the aforementioned problems, we investigated and sought approaches from the sensor data generated by human-mobile interactions. The data are collected from the on-board sensors, including voice data from microphone, acceleration data from accelerometer, angular acceleration data from gyroscope, magnetic force data from magnetometer, and multi-touch gesture input data from touchscreen. We studied the feasibility of extracting biometric and behaviour features from the on-board sensor data and how to efficiently employ the features extracted to perform user-identity verification on the smartphone device. Based on the experimental results of the single-sensor modalities, we further investigated how to integrate them with hardware such as fingerprint and Trust Zone to practically fulfill a usable identity management system for both local application and remote services control. User studies and on-device testing sessions were held for privacy and usability evaluation.Computer Science, Department o

Android Based Behavioral Biometric Authentication via Multi-Modal Fusion

Because mobile devices are easily lost or stolen, continuous authentication is extremely desirable for them. Behavioral biometrics provides non-intrusive continuous authentication that has much less impact on usability than active authentication. However single-modality behavioral biometrics has proven less accurate than standard active authentication. This thesis presents a behavioral biometric system that uses multi-modal fusion with user data from touch, keyboard, and orientation sensors. Testing of ve users shows that fusion of modalities provides more accurate authentication than each individual modalities by itself. Using the BayesNet classification algorithm, fusion achieves False Acceptance Rate (FAR) and False Rejection Rate (FRR) values of 9.65% and 2% respectively, each of which is 8% lower than the closest individual modality

Enhancing Energy Efficiency and Privacy Protection of Smart Devices

Smart devices are experiencing rapid development and great popularity. Various smart products available nowadays have largely enriched people’s lives. While users are enjoying their smart devices, there are two major user concerns: energy efficiency and privacy protection. In this dissertation, we propose solutions to enhance energy efficiency and privacy protection on smart devices. First, we study different ways to handle WiFi broadcast frames during smartphone suspend mode. We reveal the dilemma of existing methods: either receive all of them suffering high power consumption, or receive none of them sacrificing functionalities. to address the dilemma, we propose Software Broadcast Filter (SBF). SBF is smarter than the “receive-none” method as it only blocks useless broadcast frames and does not impair application functionalities. SBF is also more energy efficient than the “receive-all” method. Our trace driven evaluation shows that SBF saves up to 49.9% energy consumption compared to the “receive-all” method. Second, we design a system, namely HIDE, to further reduce smartphone energy wasted on useless WiFi broadcast frames. With the HIDE system, smartphones in suspend mode do not receive useless broadcast frames or wake up to process use- less broadcast frames. Our trace-driven simulation shows that the HIDE system saves 34%-75% energy for the Nexus One phone when 10% of the broadcast frames are useful to the smartphone. Our overhead analysis demonstrates that the HIDE system has negligible impact on network capacity and packet round-trip time. Third, to better protect user privacy, we propose a continuous and non-invasive authentication system for wearable glasses, namely GlassGuard. GlassGuard discriminates the owner and an imposter with biometric features from touch gestures and voice commands, which are all available during normal user interactions. With data collected from 32 users on Google Glass, we show that GlassGuard achieves a 99% detection rate and a 0.5% false alarm rate after 3.5 user events on average when all types of user events are available with equal probability. Under five typical usage scenarios, the system has a detection rate above 93% and a false alarm rate below 3% after less than 5 user events

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