The technical requirements for securing safety-related applications in connected autonomous vehicles (CAVs) include security (e.g., authentication, integrity, non-repudiation depending on the specific applications), privacy (e.g., anonymity and unlinkability) and computing efficiency of the solutions designed to address security and privacy aspects. Several cryptographic techniques have been considered in the literature to meet these technical requirements. A notable category of these techniques is often referred to as pseudonym schemes in the context of CAVs, which aim to address security and privacy simultaneously. This paper provides an overview of the state-of-the-art research on pseudonym techniques for CAVs, including a comparative evaluation of their performance in the context of two representative safety-related CAV applications: Cooperative positioning and intersection collision avoidance. This study aims to guide the effective adoption of such schemes for various applications in CAVs. In this paper, three main categories of pseudonym schemes are considered: public key schemes, identity-based signatures, and group signatures. We compare these schemes with respect to security and privacy requirements as identified for several CAV applications. We also implement several representative pseudonym schemes in each category to evaluate their processing efficiency for signing and verifying messages used in CAV applications to provide insight into their applicability for CAV applications.

Autonomous robotic systems systems are both safety- and security-critical, since a breach in system security may impact safety. In such critical systems, formal verification is used to model the system and verify that it obeys specific functional and safety properties. Independently, threat modelling is used to analyse and manage the cyber security threats that such systems may encounter. Both verification and threat analysis serve the purpose of ensuring that the system will be reliable, albeit from differing perspectives. In prior work, we argued that these analyses should be used to inform one another and, in this paper, we extend our previously defined methodology for security-minded verification by incorporating runtime verification. To illustrate our approach, we analyse an algorithm for sending Cooperative Awareness Messages between autonomous vehicles. Our analysis centres on identifying STRIDE security threats. We show how these can be formalised, and subsequently verified, using a combination of formal tools for static aspects, namely Promela/SPIN and Dafny, and generate runtime monitors for dynamic verification. Our approach allows us to focus our verification effort on those security properties that are particularly important and to consider safety and security in tandem, both statically and at runtime.

Cloud-Assisted Connected and Autonomous Vehicles (CCAV) are set to revolutionise road safety, providing substantial societal and economic advantages. However, with the evolution of CCAV technology, security and privacy threats have increased. Although several studies have been published around the threat and risk estimation aspects of CCAV, limited research exists on the security implications and emerging threat landscapes in the CCAV platooning application. We conducted an extensive review and categorisation of real-world security incidents and created an account of 132 threats from scholarly sources and 64 threats from recorded events in practice. Furthermore, we defined thirty-one (31) trust domains and outlined eight (8) unique attack vectors to supplement existing research efforts for the systematic security analysis of such cyberinfrastructures. Using these findings, we create a detailed attack taxonomy to communicate threat-related information in CCAV and platooning applications and highlight emerging challenges and ways to safeguard the broader CCAV systems. This work acts as a roadmap to existing researchers and practitioners advocating for a ‘security and privacy by design’ framework for a dynamically evolving CCAV threat landscape.

As threat vectors and adversarial capabilities evolve, Cloud-Assisted Connected and Autonomous Vehicles (CCAVs) are becoming more vulnerable to cyberattacks. Several established threat analysis and risk assessment (TARA) methodologies are publicly available to address the evolving threat landscape. However, these methodologies inadequately capture the threat data of CCAVs, resulting in poorly defined threat boundaries or the reduced efficacy of the TARA. This is due to multiple factors, including complex hardware–software interactions, rapid technological advancements, outdated security frameworks, heterogeneous standards and protocols, and human errors in CCAV systems. To address these factors, this study begins by systematically evaluating TARA methods and applying the Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privileges (STRIDE) threat model and Damage, Reproducibility, Exploitability, Affected Users, and Discoverability (DREAD) risk assessment to target system architectures. This study identifies vulnerabilities, quantifies risks, and methodically examines defined data processing components. In addition, this study offers an attack tree to delineate attack vectors and provides a novel defense taxonomy against identified risks. This article demonstrates the efficacy of the TARA in systematically capturing compromised security requirements, threats, limits, and associated risks with greater precision. By doing so, we further discuss the challenges in protecting hardware–software assets against multi-staged attacks due to emerging vulnerabilities. As a result, this research informs advanced threat analyses and risk management strategies for enhanced security engineering of cyberphysical CCAV systems.

This study investigates impact on customers' perceptions due to a cyber-attack by mapping STRIDE threat modelling to confidentiality, integrity, and availability (CIA) cybersecurity requirements of an e-commerce platform. Further, it studies the influence of loyalty programs and its customer segmentation. The study is based on response from 120 e-commerce customers. Results indicate, confidentiality compromise has the highest negative impact on customers' perception. About 82% respondents expressed perceived privacy risk and concerns as most impacted due to a cyber-attack. Despite these concerns, non-loyal customers are more concerned about purchasing securely by at least 10%. Whereas, loyal customers are affected more emotionally by at most 13%. These findings contribute to the body of literature on customer perception of security, privacy, and trust due to a cyber-attack on digital platforms (such as e-commerce websites) impacting the organisational reputation within the wider digital economy. The result of this work is valuable for information security and socio-technical experts, as well as regulatory authorities, in the progressing digital economy.

Machine and deep learning algorithms have become widely employed in natural language processing tasks that involve text categorisation. Behavior analysis has proven helpful for solving human problems, while sentiment analysis is helpful to extract opinions. This paper aims to develop a behavioral analysis tool for solving problems rooted in behavior by leveraging survey comment data. The study categorises behaviours based on well-known behavioral barriers and employs Natural Language Processing (NLP) techniques to comprehend and interpret the comment data. Pre-processing techniques are applied to extract information from the texts. Subsequently, feature extraction is performed using Term Frequency-Inverse Document Frequency (TF-IDF) word vectorization, converting the texts into numerical values. Various Machine Learning (ML) techniques, including Multinomial Naive Bayes, XGBoost, Linear Support Vector Classifier, and Multi-Layer Perceptron Classifier, are utilized with the vectorized input and corresponding labels. Sentiments from the survey comments are extracted using a pre-trained hugging face transformer model based on the RoBERTa platform. However, limited availability of data from the health sector due to privacy and legal reasons restricts its use. Nevertheless, the model can be used to personalise prevention plans, including lifestyle and early medical interventions. It can be used in academia and e-commerce industry to identify trends and patterns by analysing user traffic and online behaviour, enabling the development of personalised recommendations. These examples highlight the potential for research to contribute to the digital economy across diverse fields.

Digital Identity (DI) technologies have the potential to enhance the quality of life of citizens through the provision of seamless services, improve the effectiveness of public services, and increase overall economic competitiveness. However, lack of access to DIs can limit these benefits, while unequal access can lead to uneven distribution of these benefits across social groups and escalate existing tensions. Accessible, user-friendly and efficient onboarding can play a key role in ensuring equitable access and wide adoption of DI technologies. This paper proposes the development of physical locations (Experience Centres) that can be used for citizen onboarding to national DI systems, positively shaping citizens' first impression with the technology and, in turn, promoting adoption. To this end, we outline a multidisciplinary research approach for identifying and addressing the considerations necessary for designing, developing and operating a model Experience Centre for DI onboarding in an inclusive manner.

Modular Open Source Identity Platform (MOSIP) aims to provide system uniqueness, interoperability, affordability, and privacy-bydesign while preventing vendor lock-in. These features benefit citizens of developing nations. If an adversary with malicious intent exploits the system, they could breach security and privacy requirements, thereby causing reputational damage. As a result, it is necessary to understand and re-design the system by implementing security and privacy measures. This paper performs threat analysis and risk assessment for the pre-registration, registration, and authentication phases of MOSIP. STRIDE/DREAD methodology is applied for each phase to identify, quantify and analyse threats. We have identified appropriate measures and proposed a suitable risk mitigation strategy based on the requirements of each stage. Researchers can use this study to understand the threats and develop novel safeguards systematically. This research would contribute to developing a system that adheres to security and privacy requirements, and is therefore more resilient and reliable. Such attempts would help in developing the capability to assess the system trustworthiness for developing nations, benefiting the broader digital economy.

A growing number of adversaries are targeting space missions, and as such, there have been increasing academic and industrial efforts in identifying threats and risks through modelling techniques. In parallel, the research communities are collaborating to lower the entry barriers for space activities to deliver more innovative and cost-effective space missions. This evolution has been termed as New Space. However, this transformation of the space ecosystem has led to changes in the threat landscape, introducing new threat vectors and threat actors intent on compromising space systems and missions. As a result, it is expected that cyber threats could increase against space systems. Furthermore, teleoperation, a significant use case for building extraterrestrial habitats, has already been shown vulnerable in other domains as well. For example, teleoperated robots developed for remote surgery have been shown to be vulnerable to threats, such as malicious control due to an elevation-of-privilege attack. Threat modelling is a systematic and structured method to determine associated system vulnerabilities, possible attack entry points and vectors, and potential impacts on the system. In this work, we examine the efficacy of the de facto threat modelling methods such as STRIDE/DREAD in capturing highly adaptive security requirements and threats from a system-centric perspective for the teleoperation mission scenario. Understanding and protecting these hardware-software assets and their interaction in the mission is of foremost importance since security breaches threaten human safety across the broader New Space ecosystem. This research presents the limitations of existing threat modelling approaches in capturing hardware-software interaction in space systems, which is an open area for scientific enquiry. Moreover, research challenges are raised to improve the safety and security of the teleoperation mission. The output of this work can then be used to develop more appropriate threat modelling approaches to support security requirement engineering for different New Space mission scenarios.

In-orbit construction and maintenance of large-scale structures such as the International Space Station (ISS) have led to increasing interest in developing effective and efficient teleoperation systems. However, teleoperation requires communication of data between the operator and the operation site which may be subject to cyber-attacks. Cyber security measures such as encryption and hashing can prevent these attacks from being successful however, these security measures create additional overheads in communications that can impact on the operators ability to effectively control the operations at a distance. This paper investigates the time delay impact of encryption security controls and presents an analysis of the impact of different encryption algorithms on the communication channel. An awareness of the performance cost of encryption methods allows mission planners to consider the risk of security compromise and balance this against performance costs.

Vehicle platooning (a group of two or more consecutive connected autonomous vehicles that travel simultaneously at the same velocity with a short inter-vehicular distance based on vehicle to vehicle communication) has significant potential to advance traffic, including enhancing travel safety, improving traffic efficacy and decreasing energy consumption. Much focus has been put on developing machine learning-based autonomous driving systems. However, the interactions between humans and the autonomous driving system have not been widely studied, although understanding the human factor is critical as that can cause human errors and potential accidents. Besides, vehicle platooning introduces a new cooperative driving paradigm for drivers. From such circumstances may emerge a new pattern for human interaction with the vehicle platoons. This study presents a semisystematic methodology to review existing studies of human factors in vehicle platoons. Among the human factors, user acceptance and trust significantly impact the sustained development of autonomous driving and concerned user satisfaction. Achieving higher user satisfaction can present business advantages for vehicle platooning service providers in the future. In this paper, the human-vehicle platoon interaction is classified into three groups: pedestrians, other drivers and in-platoon driver interaction. Then the research gaps are highlighted for the field.

Identity empowers global citizens; it helps an individual to play an active part in international development. To do so, there is often a need for individuals to formally identify themselves. This empowers them to engage in financial services, voting rights, business rights, land titles, social protection, school and various other benefits. Organisations are becoming increasingly customer-centric and many are realising the importance of digital identity as a key to improve customer experience. However, a significant proportion of the human population does not have access to credentials, and in a number of instances, identities have been counterfeited or misused. Robust identification and associated management systems need to be introduced and implemented to address these challenges. Novel digitised decentralised approaches are being explored, and this emerging field promises to empower individuals with an identity; however, emerging threats and risks prevent such systems from being trusted. Firstly, this paper aims to describe the existing model of evolving Digital Identity Management System(IDMS) and investigate the related cyber security requirements and challenges to such systems. Secondly, the STRIDE threat modelling approach is described in the context of IDMS. Finally, the paper identifies the threat landscape of IDMS by employing the threat modelling approach.

The Internet of Things (IoT) is rapidly evolving, and its need for low-latency communication, instant computation and dynamic security demands a novel approach. Three-tier architectures comprising a centralised cloud, decentralised edge cloud and end nodes, is increasingly being considered to address these requirements. The Internet of Vehicles (IoV) is a particularly important flavour of the IoT that is attracting significant academic, governmental and industrial interest. This research considers a three-tier architecture approach to providing edge cloud-assisted connected and autonomous vehicles. The paper describes the characteristics of edge cloud-assisted CAV systems and presents some associated applications based upon the architecture. We investigate the security requirements for such systems. We describe a new method for prioritising Cooperative Awareness Messages(CAMs) based upon these requirements in the edge cloud.

In this paper, a novel model for the cyber-security analysis of Level 3 (L3) Automated Driving (AD) systems is proposed by integrating aspects of functional safety. The model is built based on the state-of-the-art framework for cyber security analysis, known as Threat Analysis and Risk Assessment (TARA), which quantifies the likelihood and the impact of attack and combines them in order to derive an attack risk value. The novelty lies in the bespoke integration of the impact calculation, which incorporates the notion of controllability of an attack by the AD system and/or by the driver. The proposed model is applied for the Urban Chauffeur and the Highway Chauffeur AD system functions, providing insights into the security risk in a wide area of distinct operational design domains as defined by SAE J3016. Remote attack surfaces (e.g., modifications of road infrastructure) are also taken into account in the analysis.

Connected and Autonomous Vehicles (CAVs) bring situational awareness to vehicles promising a safer transportation system. To support collaborative awareness, cloud-assisted CAV are being developed besides Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. CAVs require efficient and accurate information from numerous sources, internal and external to the vehicle, for time-critical safety applications. As such, cloud-assisted CAVs would be exposed to a large amount of dynamic information (such as speed, location, vehicle ID, travel routes, passengers' personal information, bank details and so on). Moreover, CAVs have a reasonably long lifetime due to which the associated vehicular technologies in CAVs age and it is possible that vulnerabilities can be exploited to inflict harm. As a result, when technologies change, the security requirements evolve. This research aims to discuss significant research challenges, classify emerging threats, attacks and countermeasures and identify immediate research directions to protect the evolving cloud-assisted CAVs with security mechanisms to adapt to complex and dynamic environments.

Intelligent Transportation Systems (ITS) is a rapidly growing research space with many issues and challenges. One of the major concerns is to successfully integrate connected technologies, such as cloud infrastructure and edge cloud, into ITS. Security has been identified as one of the greatest challenges for the ITS, and security measures require consideration from design to implementation. This work focuses on providing an analysis of cyber risk and associated concentration of research (ACR 2 ). The introduction of ACR 2 approach can be used to consider research challenges in VEC and open up further investigation into those threats that are important but under-researched. That is, the approach can identify very high or high risk areas that have a low research concentration. In this way, this research can lay the foundations for the development of further work in securing the future of ITS.

The interaction between discrete components of Internet of Things (IoT) and Intelligent Transportation Systems (ITS) is vital for a collaborative system. The secure and reliable use of Cruise Control (CC) with Cloud and Edge Cloud to achieve complete autonomy for a vehicle is a key component and a major challenge for ITS. This research unravels the complications that arise when Adaptive Cruise Control (ACC) is incorporated into a collaborative environment. It mainly answers the question of where to securely compute Collaborative Cruise Control's (CCC) data in a connected environment. To address this, the paper initially reviews previous research in the domain of Vehicular Cloud, ITS architecture, related threat modelling approaches, and secure implementations of ACC. An overview application model for CCC is developed for performing a threat analysis with the purpose of investigating the reasons why a vehicle suffers collision. Through the use of interviews, the research analyses and suggests the location of computational data by creating a taxonomy between the Edge Cloud, Cloud and the On-board Unit (OBU) while validating the model.