Supervision tracking: improving the student and staff experience for projects and dissertations

A Supervision tracking system used for the management of projects is described and evaluated. A proposed enhanced version with applicability across a university, and for dissertations as well as projects, is presented. The benefits of the current and proposed systems are articulated

Risk estimation for a secure and usable user authentication mechanism for mobile passenger ID devices

User Authentication in mobile devices acts as a first line of defense verifying the user's identity to allow access to the resources of a device and typically was based on “something the user knows”, known also as knowledge-based user authentication for several decades. However, recent studies point out that although knowledge-based user authentication has been the most popular for authenticating an individual, nowadays it is no more considered secure and convenient for the mobile user as it is imposing several limitations in terms of security and usability. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Toward this direction, user authentication based on the “something the user is” has caught the attention. This category includes authentication methods which make use of human physical characteristics (also referred to as physiological biometrics), or involuntary actions (also referred to as behavioral biometrics). In particular, risk-based user authentication based on behavioral biometrics appears to have the potential to increase the reliability of authentication without sacrificing usability. In this context, we focus on the estimation of the risk score, in a continuous mode, of the risk-based user authentication mechanism that we have proposed in our previous work for mobile passenger identification (ID) devices for land/sea border control

Generating datasets based on the HuMIdb dataset for risk-based user authentication on Smartphones

User authentication acts as the first line of defense verifying the identity of a mobile user, often as a prerequisite to allow access to resources in a mobile device. Risk-based user authentication based on behavioral biometrics appears to have the potential to increase mobile authentication security without sacrificing usability. Nevertheless, in order to precisely evaluate classification and/or novelty detection algorithms for risk-based user authentication, it is of utmost importance to make use of quality datasets to train and test these algorithms. To the best of our knowledge, there is a lack of up-to-date, representative and comprehensive datasets that are publicly available to the research community for effective training and evaluation of classification and/or novelty detection algorithms suitable for risk-based user authentication. Toward this direction, in this paper, the aim is to provide details on how we generate datasets based on HuMIdb dataset for training and testing classification and novelty detection algorithms for risk-based adaptive user authentication. The HuMIdb dataset is the most recent and publicly available dataset for behavioral user authentication

Behavioral biometrics for mobile user authentication: benefits and limitations

User authentication serves as the primary defense, also referred to as first line of defense, by verifying the identity of a mobile user, often as a requirement for accessing resources on a mobile device. For many years, user authentication relied on “something that the user knows,” also known as knowledge-based user authentication. However, recent research indicates that knowledge-based user authentication is no longer considered secure or convenient for mobile users because it imposes several limitations. These limitations highlight the need for more secure and user-friendly user authentication methods. One promising solution is user authentication based on “something that the user is,” which includes authentication methods that use physical characteristics of the mobile user (i.e., physiological biometrics) or their involuntary actions (i.e., behavioral biometrics). Although physiological biometrics have been successfully deployed for mobile user authentication over the last years, recent studies suggest that they show several weaknesses (e.g., vulnerable to various attacks such as impersonation). Consequently, experts in the security field are now focusing more on user authentication based on behavioral biometrics. Therefore, the aim of this work is to investigate the benefits, as well as the limitations of behavioral biometrics for mobile user authentication in order to provide a foundation for organizing research efforts toward the design and development of proper user authentication solutions based on behavioral biometrics for mobile devices

Risk-based adaptive user authentication for mobile passenger ID devices for land/sea border control

New services and products are increasingly becoming integral parts of our daily lives rising our technological dependence, as well as our exposure to risks from cyber. Critical sectors such as transport are progressively depending on digital technologies to run their core operations and develop novel solutions to exploit the economic strengths of the European Union. However, despite the fact that the continuously increasing number of visitors, entering the European Union through land-border crossing points or seaports, brings tremendous economic benefits, novel border control solutions, such as mobile devices for passenger identification for land and sea border control, are essential to accurately identify passengers “on the fly” while ensuring their comfort. However, the highly confidential personal data managed by these devices makes them an attractive target for cyberattacks. Therefore, novel secure and usable user authentication mechanisms are required to increase the level of security of this kind of devices without interrupting border control activities. Towards this direction, we, firstly, discuss risk-based and adaptive authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Besides that, a novel risk-based adaptive user authentication mechanism is proposed for mobile passenger identification devices used by border control officers at land and sea borders