Machine learning techniques for implicit interaction using mobile sensors

Interactions in mobile devices normally happen in an explicit manner, which means that they are initiated by the users. Yet, users are typically unaware that they also interact implicitly with their devices. For instance, our hand pose changes naturally when we type text messages. Whilst the touchscreen captures finger touches, hand movements during this interaction however are unused. If this implicit hand movement is observed, it can be used as additional information to support or to enhance the users’ text entry experience. This thesis investigates how implicit sensing can be used to improve existing, standard interaction technique qualities. In particular, this thesis looks into enhancing front-of-device interaction through back-of-device and hand movement implicit sensing. We propose the investigation through machine learning techniques. We look into problems on how sensor data via implicit sensing can be used to predict a certain aspect of an interaction. For instance, one of the questions that this thesis attempts to answer is whether hand movement during a touch targeting task correlates with the touch position. This is a complex relationship to understand but can be best explained through machine learning. Using machine learning as a tool, such correlation can be measured, quantified, understood and used to make predictions on future touch position. Furthermore, this thesis also evaluates the predictive power of the sensor data. We show this through a number of studies. In Chapter 5 we show that probabilistic modelling of sensor inputs and recorded touch locations can be used to predict the general area of future touches on touchscreen. In Chapter 7, using SVM classifiers, we show that data from implicit sensing from general mobile interactions is user-specific. This can be used to identify users implicitly. In Chapter 6, we also show that touch interaction errors can be detected from sensor data. In our experiment, we show that there are sufficient distinguishable patterns between normal interaction signals and signals that are strongly correlated with interaction error. In all studies, we show that performance gain can be achieved by combining sensor inputs

Multi-Factor Authentication: A Survey

Today, digitalization decisively penetrates all the sides of the modern society. One of the key enablers to maintain this process secure is authentication. It covers many different areas of a hyper-connected world, including online payments, communications, access right management, etc. This work sheds light on the evolution of authentication systems towards Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). Particularly, MFA is expected to be utilized for human-to-everything interactions by enabling fast, user-friendly, and reliable authentication when accessing a service. This paper surveys the already available and emerging sensors (factor providers) that allow for authenticating a user with the system directly or by involving the cloud. The corresponding challenges from the user as well as the service provider perspective are also reviewed. The MFA system based on reversed Lagrange polynomial within Shamir’s Secret Sharing (SSS) scheme is further proposed to enable more flexible authentication. This solution covers the cases of authenticating the user even if some of the factors are mismatched or absent. Our framework allows for qualifying the missing factors by authenticating the user without disclosing sensitive biometric data to the verification entity. Finally, a vision of the future trends in MFA is discussed.Peer reviewe

Harnessing the Power of Generative Models for Mobile Continuous and Implicit Authentication

Authenticating a user's identity lies at the heart of securing any information system. A trade off exists currently between user experience and the level of security the system abides by. Using Continuous and Implicit Authentication a user's identity can be verified without any active participation, hence increasing the level of security, given the continuous verification aspect, as well as the user experience, given its implicit nature. This thesis studies using mobile devices inertial sensors data to identify unique movements and patterns that identify the owner of the device at all times. We implement, and evaluate approaches proposed in related works as well as novel approaches based on a variety of machine learning models, specifically a new kind of Auto Encoder (AE) named Variational Auto Encoder (VAE), relating to the generative models family. We evaluate numerous machine learning models for the anomaly detection or outlier detection case of spotting a malicious user, or an unauthorised entity currently using the smartphone system. We evaluate the results under conditions similar to other works as well as under conditions typically observed in real-world applications. We find that the shallow VAE is the best performer semi-supervised anomaly detector in our evaluations and hence the most suitable for the design proposed. The thesis concludes with recommendations for the enhancement of the system and the research body dedicated to the domain of Continuous and Implicit Authentication for mobile security

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Contains fulltext : 187230.pdf (preprint version ) (Open Access

Secure Authentication for Mobile Users

RÉSUMÉ :L’authentification biométrique telle que les empreintes digitales et la biométrie faciale a changé la principale méthode d’authentification sur les appareils mobiles. Les gens inscrivent facilement leurs modèles d’empreintes digitales ou de visage dans différents systèmes d’authentification pour profiter de leur accès facile au smartphone sans avoir besoin de se souvenir et de saisir les codes PIN/mots de passe conventionnels. Cependant, ils ne sont pas conscients du fait qu’ils stockent leurs caractéristiques physiologiques ou comportementales durables sur des plates-formes non sécurisées (c’est-à-dire sur des téléphones mobiles ou sur un stockage en nuage), menaçant la confidentialité de leurs modèles biométriques et de leurs identités. Par conséquent, un schéma d’authentification est nécessaire pour préserver la confidentialité des modèles biométriques des utilisateurs et les authentifier en toute sécurité sans compter sur des plates-formes non sécurisées et non fiables.La plupart des études ont envisagé des approches logicielles pour concevoir un système d’authentification sécurisé. Cependant, ces approches ont montré des limites dans les systèmes d’authentification sécurisés. Principalement, ils souffrent d’une faible précision de vérification, en raison des transformations du gabarit (cancelable biometrics), de la fuite d’informations (fuzzy commitment schemes) ou de la réponse de vérification non en temps réel, en raison des calculs coûteux (homomorphic encryption).---------- ABSTRACT: Biometric authentication such as fingerprint and face biometrics has changed the main authentication method on mobile devices. People easily enroll their fingerprint or face template on different authentication systems to take advantage of their easy access to the smartphone with no need to remember and enter the conventional PINs/passwords. However, they are not aware that they store their long-lasting physiological or behavioral characteristics on insecure platforms (i.e., on mobile phones or on cloud storage), threatening the privacy of their biometric templates and their identities. Therefore, an authentication scheme is required to preserve the privacy of users’ biometric templates and securely authenticate them without relying on insecure and untrustworthy platforms. Most studies have considered software-based approaches to design a privacy-reserving authentication system. However, these approaches have shown limitations in secure authentication systems. Mainly, they suffer from low verification accuracy, due to the template transformations (in cancelable biometrics), information leakage (in fuzzy commitment schemes), or non real-time verification response, due to the expensive computations (in homomorphic encryption)

Social, Private, and Trusted Wearable Technology under Cloud-Aided Intermittent Wireless Connectivity

There has been an unprecedented increase in the use of smart devices globally, together with novel forms of communication, computing, and control technologies that have paved the way for a new category of devices, known as high-end wearables. While massive deployments of these objects may improve the lives of people, unauthorized access to the said private equipment and its connectivity is potentially dangerous. Hence, communication enablers together with highly-secure human authentication mechanisms have to be designed.In addition, it is important to understand how human beings, as the primary users, interact with wearable devices on a day-to-day basis; usage should be comfortable, seamless, user-friendly, and mindful of urban dynamics. Usually the connectivity between wearables and the cloud is executed through the user’s more power independent gateway: this will usually be a smartphone, which may have potentially unreliable infrastructure connectivity. In response to these unique challenges, this thesis advocates for the adoption of direct, secure, proximity-based communication enablers enhanced with multi-factor authentication (hereafter refereed to MFA) that can integrate/interact with wearable technology. Their intelligent combination together with the connection establishment automation relying on the device/user social relations would allow to reliably grant or deny access in cases of both stable and intermittent connectivity to the trusted authority running in the cloud.The introduction will list the main communication paradigms, applications, conventional network architectures, and any relevant wearable-specific challenges. Next, the work examines the improved architecture and security enablers for clusterization between wearable gateways with a proximity-based communication as a baseline. Relying on this architecture, the author then elaborates on the social ties potentially overlaying the direct connectivity management in cases of both reliable and unreliable connection to the trusted cloud. The author discusses that social-aware cooperation and trust relations between users and/or the devices themselves are beneficial for the architecture under proposal. Next, the author introduces a protocol suite that enables temporary delegation of personal device use dependent on different connectivity conditions to the cloud.After these discussions, the wearable technology is analyzed as a biometric and behavior data provider for enabling MFA. The conventional approaches of the authentication factor combination strategies are compared with the ‘intelligent’ method proposed further. The assessment finds significant advantages to the developed solution over existing ones.On the practical side, the performance evaluation of existing cryptographic primitives, as part of the experimental work, shows the possibility of developing the experimental methods further on modern wearable devices.In summary, the set of enablers developed here for wearable technology connectivity is aimed at enriching people’s everyday lives in a secure and usable way, in cases when communication to the cloud is not consistently available