Session management in distributed Internet services is traditionally based on username and password, explicit logouts and mechanisms of user session expiration using classic timeouts. Emerging biometric solutions allow substituting username and password with biometric data during session establishment, but in such an approach still a single verification is deemed sufficient, and the identity of a user is considered immutable during the entire session. Additionally, the length of the session timeout may impact on the usability of the service and consequent client satisfaction. This paper explores promising alternatives offered by applying biometrics in the management of sessions. A secure protocol is defined for perpetual authentication through continuous user verification. The protocol determines adaptive timeouts based on the quality, frequency and type of biometric data transparently acquired from the user. The functional behavior of the protocol is illustrated through Matlab simulations, while model-based quantitative analysis is carried out to assess the ability of the protocol to contrast security attacks exercised by different kinds of attackers. Finally, the current prototype for PCs and Android smartphones is discussed.

We argue that object detectors in the safety critical domain should prioritize detection of objects that are most likely to interfere with the actions of the autonomous actor. Especially, this applies to objects that can impact the actor’s safety and reliability. To quantify the impact of object (mis)detection on safety and reliability in the context of autonomous driving, we propose new object detection measures that reward the correct identification of objects that are most dangerous and most likely to affect driving decisions. To achieve this, we build an object criticality model to reward the detection of the objects based on proximity, orientation, and relative velocity with respect to the subject vehicle. Then, we apply our model on the recent autonomous driving dataset nuScenes, and we compare nine object detectors. Results show that, in several settings, object detectors that perform best according to the nuScenes ranking are not the preferable ones when the focus is shifted on safety and reliability.

Personal information is continuously gathered and processed by modern web applications. Due to regulation laws and to protect the privacy of users, customers, and business partners, such information must be kept private. A recurring problem in constructing web applications and services that protect privacy is the insufficient resources for documenting them. As web applications must be developed consistently with the statements of the privacy policy in order to enforce them, a structured documentation is necessary to model privacy protection during application design. To contribute with solutions to this problem, in this paper we propose a UML profile for privacy-aware applications. This profile helps building UML models that specify and structure particular concepts of privacy and, consequently, improve privacy definition and enforcement. After introducing the main privacy concepts, we describe how they are represented in the UML language. The profile's ability to model statements of realistic privacy policies is then demonstrated on a case study.

Model-Driven engineering (MDE) aims to elevate models in the engineering process to a central role in the specification, design, integration, validation, and operation of a system. MDE is becoming a widely used approach within the dependability domain: the system, together with its main dependability-related characteristics, is represented by engineering language models, while automatic transformations are used to generate the analysis models for the dependability analyses. This paper discusses the dependability concerns that should be captured by engineering languages for dependability analysis. It motivates and defines a conceptual model where the specific dependability aspects related to specific dependability analyses can be consistently and unambiguously merged, also detailing the part of the conceptual model supporting state-based dependability analysis methods. Then, it introduces a new intermediate dependability model that acts as a bridge between the high-level engineering language and the low-level dependability analysis formalism, and we discuss its features and its expressive power showing its application for the modelling of a simple but representative case-study.

In the last ten years, Model Driven Engineering (MDE) approaches have been extensively used for the analysis of extra-functional properties of complex systems, like safety, dependability, security, predictability, quality of service. To this purpose, engineering languages (like UML and AADL) have been extended with additional features to model the required non-functional attributes, and transformations have been used to automatically generate the analysis models to be solved by appropriate analysis tools. In most of the available works, however, the transformations are not inte grated into a more general development process, aimed to support both domain-specific design analysis and verification of extra-functional properties. In this paper we explore this research direction presenting a transformation work flow for dependability analysis that is part of an industrial-quality infrastructure for the specification, analysis and verification of extra-functional properties, currently under development within the ARTEMIS-JU CHESS project. Specifically, the paper provides the following major contributions: i) definition of the required transformation steps to automatically assess the system dependability properties starting from the CHESS Modeling Language, ii) definition of a new Intermediate Dependability Model (IDM) acting as a bridge between the CHESS Modeling Language and the low-level analysis models, iii) definition of transformations from the CHESS Modeling Language to IDM models.