Smart device user behavior classification and learning with Hidden Markov model

Today, millions of people use smart phones because smart phones are convenient to access various online services, such as online social networks, online banking and emails. People usually use passwords for authentication, but passwords are also the major source of security vulnerabilities, which are easily to be guessed and re-used. So, protecting a user’s privacy on mobile is never trivial. Another issue with passwords is the usability in that users have to repeatedly enter passwords or pins. Therefore, it will be tremendously beneficial if a non-password authentication can be offered. In this thesis project, we propose a stochastic non-password authentication solution that models users’ finger gestures and handholding patterns as users’ profiles using Hidden Markov Model (HMM). The system leverages the data collected by three sensors on mobile devices: accelerometer, orientation and touch screen input measurements, and then trains an HMM user profile model. This solution is a software-only approach that does not require any authentication hardware such as fingerprint sensor. The solution employs machine learning algorithms to passively re-use the data provided by existing sensors equipped on devices. This project is implemented and evaluated on Android devices for performance assessment. In the experiment, we verify the accuracy of authentication. The result shows high accuracy by using more sensors since HMM has been reported of 89% accuracy in image-based gesture recognitions.Thesis (M.S.)Department of Computer Scienc

Activity-Based User Authentication Using Smartwatches

Smartwatches, which contain an accelerometer and gyroscope, have recently been used to implement gait and gesture- based biometrics; however, the prior studies have long-established drawbacks. For example, data for both training and evaluation was captured from single sessions (which is not realistic and can lead to overly optimistic performance results), and in cases when the multi-day scenario was considered, the evaluation was often either done improperly or the results are very poor (i.e., greater than 20% of EER). Moreover, limited activities were considered (i.e., gait or gestures), and data captured within a controlled environment which tends to be far less realistic for real world applications. Therefore, this study remedies these past problems by training and evaluating the smartwatch-based biometric system on data from different days, using large dataset that involved the participation of 60 users, and considering different activities (i.e., normal walking (NW), fast walking (FW), typing on a PC keyboard (TypePC), playing mobile game (GameM), and texting on mobile (TypeM)). Unlike the prior art that focussed on simply laboratory controlled data, a more realistic dataset, which was captured within un-constrained environment, is used to evaluate the performance of the proposed system. Two principal experiments were carried out focusing upon constrained and un-constrained environments. The first experiment included a comprehensive analysis of the aforementioned activities and tested under two different scenarios (i.e., same and cross day). By using all the extracted features (i.e., 88 features) and the same day evaluation, EERs of the acceleration readings were 0.15%, 0.31%, 1.43%, 1.52%, and 1.33% for the NW, FW, TypeM, TypePC, and GameM respectively. The EERs were increased to 0.93%, 3.90%, 5.69%, 6.02%, and 5.61% when the cross-day data was utilized. For comparison, a more selective set of features was used and significantly maximize the system performance under the cross day scenario, at best EERs of 0.29%, 1.31%, 2.66%, 3.83%, and 2.3% for the aforementioned activities respectively. A realistic methodology was used in the second experiment by using data collected within unconstrained environment. A light activity detection approach was developed to divide the raw signals into gait (i.e., NW and FW) and stationary activities. Competitive results were reported with EERs of 0.60%, 0% and 3.37% for the NW, FW, and stationary activities respectively. The findings suggest that the nature of the signals captured are sufficiently discriminative to be useful in performing transparent and continuous user authentication.University of Kuf

A review of smart homes in healthcare

The technology of Smart Homes (SH), as an instance of ambient assisted living technologies, is designed to assist the homes’ residents accomplishing their daily-living activities and thus having a better quality of life while preserving their privacy. A SH system is usually equipped with a collection of inter-related software and hardware components to monitor the living space by capturing the behaviour of the resident and understanding his activities. By doing so the system can inform about risky situations and take actions on behalf of the resident to his satisfaction. The present survey will address technologies and analysis methods and bring examples of the state of the art research studies in order to provide background for the research community. In particular, the survey will expose infrastructure technologies such as sensors and communication platforms along with artificial intelligence techniques used for modeling and recognizing activities. A brief overview of approaches used to develop Human–Computer interfaces for SH systems is given. The survey also highlights the challenges and research trends in this area