We develop a comprehensive threat model for the automotive domain. It is accomplished by means of a novel, multilevel research methodology that leverages Human-Artificial Intelligence (HAI). Given the inherent complexity of threat modelling and the challenges in ensuring its completeness, the methodology combines the complementary strengths of human analysis with large language models over four phases. Each phase is structured as a sequence of two or three refinement levels so that each level iteratively enhances prior results through either human or artificial intelligence. The first phase focuses on modelling the system under analysis to establish a clear and structured baseline. The second phase addresses the elicitation of assets and associated threats, followed by a third phase in which mitigation strategies are designed. The fourth and final phase ensures that mitigation is augmented to explicitly incorporate Zero Trust, Pseudonymisation, and Data Minimisation within the context of the automotive domain. The methodology maintains its multilevel HAI structure across all phases, thereby fostering a dynamic validation loop between expert knowledge and machine-driven inference, ultimately enhancing both accuracy and coverage of the resulting threat model.

Within the dynamic realm of Intelligent Transportation Systems (ITS), the Internet of Vehicles (IoV) marks a significant paradigm shift. IoV is an interconnected network of vehicles, infrastructures, and the Internet, driven by wireless communication technologies. This paper dissects the privacy landscapes of ITS and IoV, exploring gaps and redundancies in standards and academic literature. We do so by leveraging European Telecommunications Standards Institute (ETSI) ITS G5 standards and IoV analyses from literature, and building two relational models to depict said privacy landscapes. A macroscopic analysis reveals structural and thematic differences: ITS, governed by established standards, has a robust structure, while IoV, in its nascent stage, lacks formalisation. A detailed analysis highlights challenges in data collection, sharing, and privacy policies. As ITS transitions to IoV, increasing data volume demands enhanced privacy safeguards. Addressing these challenges requires collaborative efforts to develop comprehensive privacy policies, prioritise user awareness, and integrate privacy-by-design principles. This paper offers insights into navigating the evolving landscape of transportation technologies, laying the groundwork for privacy-preserving ITS and IoV ecosystems.

The widespread integration of Internet of Things (IoT) devices in households generates extensive digital footprints, notably within Smart Home ecosystems.These IoT devices, brimming with data about residents, inadvertently offer insights into human activities, potentially embodying even criminal acts, such as a murder.As technology advances, so does the concern for criminals seeking to exploit various techniques to conceal evidence and evade investigations.This paper delineates the application of Anti-Digital Forensics (ADF) in Smart Home scenarios and recognises its potential to disrupt (digital) investigations.It does so by elucidating the current challenges and gaps and by arguing, in response, the conceptualisation of an ADF Kill Chain tailored to Smart Home ecosystems.While seemingly arming criminals, the Kill Chain will allow a better understanding of the distinctive peculiarities of Anti-Digital Forensics in Smart Home scenario.This understanding is essential for fortifying the Digital Forensics process and, in turn, developing robust countermeasures against malicious activities.

This paper questions how to approach threat modelling in the automotive domain at both an abstract level that features no domain-specific entities such as the CAN bus and, separately, at a detailed level. It addresses such questions by contributing a systematic method that is currently affected by the analyst's subjectivity because most of its inner operations are only defined informally. However, this potential limitation is overcome when candidate threats are identified and left to everyone's scrutiny. The systematic method is demonstrated on the established LINDDUN threat modelling methodology with respect to 4 pivotal works on privacy threat modelling in automotive. As a result, 8 threats that the authors deem not representable in LINDDUN are identified and suggested as possible candidate extensions to LINDDUN. Also, 56 threats are identified providing a detailed, automotive-specific model of threats.

Abstract NEANIAS is a research and innovation action project funded by the European Union under the Horizon 2020 program. The project addresses the challenge of prototyping novel solutions for the underwater, atmospheric and space research communities, creating a collaborative research ecosystem, and contributing to the effective materialization of the European Open Science Cloud (EOSC). NEANIAS drives the co-design, implementation, delivery, and integration into EOSC of innovative thematic and core services, derived from state-of-the-art assets and practices in the target scientific communities. We present the overall NEANIAS ecosystem architecture, with an emphasis on its core visualization services, detailing their specifications and software development plan, and focusing on the underpinning service-oriented architecture for their delivery. We report on the underlying ideas and guiding principles for designing such visualization services, outlining their current release status and future development roadmaps towards Technological Readiness Level (TRL) 8 maturity and EOSC integration.