Vehicles are currently being developed and sold with increasing levels of connectivity and automation. As with all networked computing devices, increased connectivity often results in a heightened risk of a cyber security attack. Furthermore, increased automation exacerbates any risk by increasing the opportunities for the adversary to implement a successful attack. In this paper, a large volume of publicly accessible literature is reviewed and compartmentalized based on the vulnerabilities identified and mitigation techniques developed. This review highlighted that the majority of studies are reactive and vulnerabilities are often discovered by friendly adversaries (white-hat hackers). Many gaps in the knowledge base were identified. Priority should be given to address these knowledge gaps to minimize future cyber security risks in the connected and autonomous vehicle sector.

Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.

In the rapidly evolving digital landscape, the generation of fake visual, audio, and textual content poses a significant threat to society’s trust, political stability, and integrity of information. The generation process has been enhanced and simplified using Artificial Intelligence techniques, which have been termed deepfake . Although significant attention has been paid to visual and audio deepfakes, there is also a burgeoning need to consider text-based deepfakes. Due to advancements in natural language processing and large language models, the potential of manipulating textual content to reshape online discourse and misinformation has increased. This study comprehensively examines the multifaceted nature and impacts of deep-fake-generated media. This work explains the broad implications of deepfakes in social, political, economic, and technological domains. State-of-the-art detection methodologies for all types of deepfake are critically reviewed, highlighting the need for unified, real-time, adaptable, and generalised solutions. As the challenges posed by deepfakes intensify, this study underscores the importance of a holistic approach that intertwines technical solutions with public awareness and legislative action. By providing a comprehensive overview and establishing a framework for future exploration, this study seeks to assist researchers, policymakers, and practitioners in navigating the complexities of deepfake phenomena.

Health data are being increasingly sensed from the health-based wearable Internet of Things (IoT) devices, providing much-needed fitness and health tracking. However, data generated also present opportunities within computer security, specifically with biometric systems used for identification and authentication purposes. This article performs a systematic review of health-based IoT data collected from wearable IoT technology. This involved performing research in the underlying data sources, what they are collected for in terms of their health monitoring, and the underlying data characteristics. Furthermore, it explores existing work in computer security using these data sources, identifying key themes of work, key limitations, and challenges. Finally, key opportunities are provided as summaries to the potential of health-based IoT data, highlighting challenges that are yet to be addressed, which motivate areas of future work.

There any many different access-control systems, yet a commonality is that they provide flexible mechanisms to enforce different access levels. Their importance in organisations to adequately restrict resources, coupled with their use in a dynamic environment, mandates the need to routinely perform policy analysis. The aim of performing analysis is often to identify potential problematic permissions, which have the potential to be exploited and could result in data theft and unintended modification. There is a vast body of published literature on analysing access-control systems, yet as performing analysis has a strong end-user motivation and is grounded in security challenges faced in real-world systems, it is important to understand how research is developing, what are the common themes of interest, and to identify key challenges that should be addressed in future work. To the best of the authors’ knowledge, no survey has been performed to gain an understanding of empirical access-control analysis, focussing on how techniques are evaluated and how they align to the needs of real-world analysis tasks. This article provides a systematic literature review, identifying and summarising key works. Key findings are identified and discussed as areas of future work.

The Internet of Vehicles (IoV) is the network of connected vehicles and transport infrastructure units (Roadside Units (RSU)), which utilizes emerging wireless systems (4G, 5G, LTE) for the communication and sharing of information. The network of connected vehicles enables users to disseminate critical information about events happening on the road (for example accidents, traffic congestions, and hazards). The exchange of information between vehicles and roadside units could improve the driving experience and road safety, as well as help drivers to identify the hazardous and safe routes in a timely manner. The sharing of critical information between vehicles is advantageous to the driver; however, at the same time, malicious actors could mislead drivers by spreading fraudulent and fake messages. Fraudulent messages can have a negative impact on the infrastructure, and more significantly, have potential to cause threats to life. It is therefore essential that vehicles can evaluate the credibility of those who send messages (vehicles or roadside units) before taking any action. In this paper, we present TrustVote, a collaborative crowdsourcing-based vehicle reputation system that enables vehicles to evaluate the credibility of other vehicles in a connected vehicular network. The TrustVote system allows participating vehicles to hide their rating/feedback scores and the list of interacted vehicles under a homomorphic cryptographic layer, which can only be unfolded as an aggregate. The proposed approach also considers the trust weight of a vehicle providing the rating scores while computing the aggregate reputation of the vehicles. A prototype of TrustVote is developed and its performance is evaluated in terms of the computational and communication overheads.