Orientation Based Accelerometer Analysis (OBAA) for Mobile Gestures: Memorable Authentication

Mobile authentication today primarily relies onPersonal Identification Numbers (PINs). For PINs to be securefrom the majority of malicious users, it must contain a highnumber of digits and be entropic. Human memory generallystruggles when it attempts to recall highly entropic numericcodes. Gesture-based authentication using Quick Reference (QR)codes, and internally analyzed accelerometer data from mobiledevices, allow for sustaining a more user-friendly, memorable,and low expense alternative to PINs. This paper presents atechnique for users to capture movements of their mobile deviceby analyzing the orientation of devices and the speed at whichthese orientations transition via accelerometer data. Thesemotions are described as the user’s gesture. Gestures can be usedto identify a user, while QR codes can be used to indicate aspecific machine a user can attempt to authenticate with. A userstudy was performed and showed gesture-based authenticationresults in a more user preferred, entropic and memorableauthentication system in comparison to similar applications

Activity Recognition using wearable computing.

A secure, user-convenient approach to authenticate users on their mobile devices is required as current approaches (e.g., PIN or Password) suffer from security and usability issues. Transparent Authentication Systems (TAS) have been introduced to improve the level of security as well as offer continuous and unobtrusive authentication (i.e., user friendly) by using various behavioural biometric techniques. This paper presents the usefulness of using smartwatch motion sensors (i.e., accelerometer and gyroscope) to perform Activity Recognition for the use within a TAS. Whilst previous research in TAS has focused upon its application in computers and mobile devices, little attention is given to the use of wearable devices - which tend to be sensor-rich highly personal technologies. This paper presents a thorough analysis of the current state of the art in transparent and continuous authentication using acceleration and gyroscope sensors and a technology evaluation to determine the basis for such an approach. The best results are average Euclidean distance scores of 5.5 and 11.9 for users\u27 intra acceleration and gyroscope signals respectively and 24.27 and 101.18 for users\u27 inter acceleration and gyroscope activities accordingly. The findings demonstrate that the technology is sufficiently capable and the nature of the signals captured sufficiently discriminative to be useful in performing Activity Recognition

A Comprehensive Evaluation of Feature Selection for Gait Recognition Using Smartwatches

Activity recognition that recognises who a user is by what they are doing at a specific point of time is attracting an enormous amount of attention. Whilst previous research in activity recognition has focused on wearable dedicated sensors (body worn sensors) or using a smartphone’s sensors (e.g. accelerometer and gyroscope), little attention is given to the use of wearable devices – which tend to be sensor-rich highly personal technologies. This paper presents a thorough analysis of the current state of the art in transparent and continuous authentication using acceleration and gyroscope sensors and an advanced feature selection approach to select the optimal features for each user. Two experiments are conducted; the first experiment used all the extracted features (i.e., 143 unique features) while (for comparison) a more selective set of only 30 features are used in the second experiment. The best results of the first experiment are average Euclidean distance scores of 0.55 and 1.41 for users’ intra acceleration and gyroscope signals respectively and 3.33 and 5.85 for users’ inter acceleration and gyroscope activities accordingly- providing sufficient disparity in distance to suggest a strong classification performance. In comparison, the second experiment demonstrated stronger results when evaluated (at best the average Euclidean distance scores is 0.03 and 0.19 for users’ intra acceleration and gyroscope signals respectively and 1.65 and 1.1 for users’ inter acceleration and gyroscope activities). The findings demonstrate that the technology is sufficiently capable and the nature of the signals captured sufficiently discriminative to be useful in performing activity recognition. Moreover, the proposed feature selection approach could offer better results and reduce the computational overhead on digital devices

WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City

The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%

Machine Learning Models for Network Intrusion Detection and Authentication of Smart Phone Users

A thesis presented to the faculty of the Elmer R. Smith College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Science by S. Sareh Ahmadi on November 18, 2019

Real-World Smartphone-based Gait Recognition

As the smartphone and the services it provides are becoming targets of cybercrime, it is critical to secure smartphones. However, it is important security controls are designed to provide continuous and user-friendly security. Amongst the most important of these is user authentication, where users have experienced a significant rise in the need to authenticate to the device and individually to the numerous apps that it contains. Gait authentication has gained attention as a mean of non-intrusive or transparent authentication on mobile devices, capturing the information required to verify the authenticity of the user whilst the person is walking. Whilst prior research in this field has shown promise with good levels of recognition performance, the results are constrained by the gait datasets utilised being based upon highly controlled laboratory-based experiments which lack the variability of real-life environments. This paper introduces an advanced real-world smartphone-based gait recognition system that recognises the subject within real-world unconstrained environments. The proposed model is applied to the uncontrolled gait dataset, which consists of 44 users over a 7–10 day capture – where users were merely asked to go about their daily activities. No conditions, controls or expectations of particular activities were placed upon the participants. The experiment has modelled four types of motion normal walking, fast walking and down and upstairs for each of the users. The evaluation of the proposed model has achieved an equal error rate of 11.38%, 11.32%, 24.52%, 27.33% and 15.08% for the normal, fast, down and upstairs and all activities respectively. The results illustrate, within an appropriate framework, that gait recognition is a viable technique for real-world use