In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characterized. Instead, new unknown threats, often referred to as zero-day attacks or zero-days, likely go undetected as they are often misclassified by those techniques. In recent years, unsupervised anomaly detection algorithms showed potential to detect zero-days. However, dedicated support for quantitative analyses of unsupervised anomaly detection algorithms is still scarce and often does not promote meta-learning, which has potential to improve classification performance. To such extent, this paper introduces the problem of zero-days and reviews unsupervised algorithms for their detection. Then, the paper applies a question-answer approach to identify typical issues in conducting quantitative analyses for zero-days detection, and shows how to setup and exercise unsupervised algorithms with appropriate tooling. Using a very recent attack dataset, we debate on i) the impact of features on the detection performance of unsupervised algorithms, ii) the relevant metrics to evaluate intrusion detectors, iii) means to compare multiple unsupervised algorithms, iv) the application of meta-learning to reduce misclassifications. Ultimately, v) we measure detection performance of unsupervised anomaly detection algorithms with respect to zero-days. Overall, the paper exemplifies how to practically orchestrate and apply an appropriate methodology, process and tool, providing even non-experts with means to select appropriate strategies to deal with zero-days.

RGB cameras are one of the most relevant sensors for autonomous driving applications. It is undeniable that failures of vehicle cameras may compromise the autonomous driving task, possibly leading to unsafe behaviors when images that are subsequently processed by the driving system are altered. To support the definition of safe and robust vehicle architectures and intelligent systems, in this paper we define the failure modes of a vehicle camera, together with an analysis of effects and known mitigations. Further, we build a software library for the generation of the corresponding failed images and we feed them to six object detectors for mono and stereo cameras and to the self-driving agent of an autonomous driving simulator. The resulting misbehaviors with respect to operating with clean images allow a better understanding of failures effects and the related safety risks in image-based applications.

In recent years, more and more efforts have been devoted in supporting the design of systems‐of‐systems (SoS). Designing such systems is a multidisciplinary problem which involves considering emergent phenomena, assuring the achievement of dependability/security requirements, guaranteeing system responsiveness, and supporting dynamicity/evolution and multicriticality of provided services. A first step towards a viable design approach is to provide a conceptual model of SoS which captures SoS concepts, and their interrelationships aiming at enhancing the understandability of SoS to stakeholders and providing the basis for further automated analysis. In this context, the AMADEOS European project is bringing together researchers and practitioners to provide the support to design SoS starting from the definition of a domain specific ontology serving as a vocabulary for SoS. Our contribution consists in the modeling of the key SoS concepts and relationships defined in AMADEOS adopting a systems modeling language visual modeling language. We propose a systems modeling language profile for SoS, and we show its applicability in a Smart Grid scenario. We show how to use the profile in a model‐driven engineering process to support different types of analyses, and we discuss how to integrate the profile in a user‐friendly model‐driven engineering tool for SoS rapid modeling, validation, code‐generation, and simulation.

In recent years, experts in the field of dependability are recognizing experimental measurements as an attractive option for assessing distributed systems; contrary to simulation, measurement allows monitoring the real execution of a system in its real usage environment. However, the results of a recent survey have highlighted that the way measurements are carried out and measurement results are expressed is far from being in line with the approach commonly adopted by metrology. The scope of this paper is twofold. The first goal is to extend the discussion on the increasing role that measurements play in dependability and on the importance of cross-fertilization between the dependability and the instrumentation and measurement communities. The second objective is to present a different approach to dependability measurements, in line with the common practices in metrology. With regard to this, the paper presents a tool for dependability measurements in distributed systems that allows evaluating the uncertainty of measurement results. The tool is an enhancement of NekoStat, which is a powerful highly portable Java framework that allows analyzing distributed systems and algorithms. Together with the description of the tool and its innovative features, two experimental case studies are presented.