Connected Automated Vehicles (CAVs) represent a transformative shift in transportation, offering enhanced safety, and efficiency. However, achieving full automation at levels four and five of the Society of Automotive Engineering (SAE) scale poses significant cybersecurity and privacy risks. To address these risks, United Nations Economic Commission for Europe (UNECE) regulations and ISO/SAE 21434 mandate Threat Analysis and Risk Assessment (TARA) as a core methodology for cyber risk management. Existing TARA frameworks, designed for conventional vehicles, fall short for higher automation levels, neglecting complexities such as the absence of human control and data-driven decision making concerns. This work, conducted within ULTIMO, a project tackling the CAVs deployment challenges, introduces TARA 2.0, an enhanced framework addressing cybersecurity, privacy, and expert subjectivity in risk assessment. A step-by-step experimental implementation demonstrates its feasibility, compliance with standards, and potential to secure the deployment of fully automated vehicles.

With the prevalence of high cyber risks within the Connected Automated Vehicle (CAV)’s environment, the core regulation bodies mandated applying Threat Analysis and Risk Assessment (TARA) methodologies. Conducting auspicious TARA is essential to ensure acceptable level of risk by analysing potential threats and determining corresponding mitigation strategies. Albeit plethora of standardised TARA versions are available, they are not-ready-to-use methods or they do not encapsulate heterogeneous CAVs properties. By considering the TARA emerging trends and the CAVs’ SAE automation levels, the present work provides a systematic study of salient TARA methodologies in the last ten years. The methodology we applied starts with a systematic review identifying TARA approaches that are relevant to the automotive domain at a large scope. After that, the methods’ applicability to CAVs is evaluated based on their threat analysis avenues and risk metrics. We elevate our appraisal further with a focus on how the automation level is considered, how the privacy impact is assessed by each TARA method, and how subjective the experts were while assessing scores to the risk metrics. Our investigation spotlights how different methods are intertwined and joint to meet the compliance with key standards such as ISO/SAE 21434. We believe that the present study’s findings identify knowledge gaps and help to shape the next generation of TARA methods to keep pace with rapidly evolving automotive technologies and support the readiness of CAV of SAE levels four and five.

The Automated City Shuttles (ACSs) aim to shape the future public transportation and provide more efficient and accessible mobility in smart cities. With the absence of a driver, such mini-busses process the sensors’ inputs and exchanged data with other vehicles and intelligent transport systems to achieve a real time assimilation of its surroundings. Consequently, the technologies supporting the driverless functionalities ushered new cybersecurity risks and data privacy breaches. Unfortunately, several studies mostly focus on individual Connected-Automated Vehicles (CAV), though intrinsic underpinnings of the ACS’s threat vectors remain unexplored. In the present paper, we considerably extend that investigation by proposing a comprehensive state of the art with farsighted analyses addressing security threats and data privacy concerns from both technical and legal perspectives to thwart potential attacks. Moreover, as existing approaches have not provided yet a clear road map about ACS’s security standards, the present work sheds light on recent and up to date standards and standardisation bodies dealing with cybersecurity and privacy issues in the automated driving ecosystem. This paper presents an analysis debating the trade-off between maximising the ACS benefits and minimising the associated security vulnerabilities and attacks through an overview of technical and legal mitigation strategies.

The H2020 European research project Safe-Guarding Home IoT Environments with Personalised Real-time Risk Control (GHOST) aims to develop a cyber-security layer on IoT smart home installations. The proposed system analyses packet-level data flows for building patterns of communications between IoT devices and external entities. To ensure non-repudiation, integrity and authentication of the data captured, they are stored in a Blockchain, a distributed ledger network, as digitally-signed transactions. Since the data can potentially include sensitive user information, it is imperative to promote trust by informing users about the operating principles of the network as well as to request the acceptance of a consent form by them. This paper presents the design and implementation of a Forms of Consent application, a Distributed Application that interacts with a set of Smart Contracts deployed on a private Ethereum network. The application is being developed as part of the GHOST project.

The European research project GHOST challenges the traditional cyber security solutions for the Internet of Things (IoT) sector by exploiting novel technologies, such as blockchain, to provide resilience and integrity of decision making on the communication exchange in a smart home context. When it comes to novel cyber security solutions for extremely heterogeneous environments like IoT and smart homes, the key focus is typically given to the understanding of network activities and elimination of suspicious traffic. The GHOST project adds an extra dimension to this approach by integrating blockchain technology at its core decision mechanism. On a daily basis, each GHOST installation is encountering malicious behaviour and suspicious IoT communications, where easy information sharing with other installations, as well as decentralised decision making, are mandatory features for the efficient protection of the end-user. GHOST's Smart Contracts (SC) are designed to tackle in an easy, yet productive way, the reporting on suspicious IP addresses which the IoT devices in a smart home are trying to communicate with. Two variations of blacklisting smart contracts are presented in this paper, covering a diverse spectrum of possible attack vectors while closely following the Privacy by Design (PbD) principles. A reputation scoring scheme for malicious IPs reporting is integrated in the SC, uncovering the implementation details on the penalisation of existing entries in case of malicious behaviour of reporting devices.