In an internet-of-things (IoT) environment, cloud computing is emerging as a technologically feasible and economically viable solution for supporting real-time and non-real-time connected vehicle (CV) applications due to its unlimited storage, enormous computing capabilities, and cost advantage, i.e., cloud computing costs less than owning such systems. However, maintaining cybersecurity is a major challenge in cloud-supported CV applications as it requires CVs and various transportation or non-transportation services to exchange data with the cloud via multiple wired and wireless communication networks, such as long-term evolution (LTE) and Wi-Fi. In this paper, we review the cybersecurity requirements of cloud-supported CV applications, such as confidentiality, integrity, availability, authentication, accountability, and privacy. Our review also identifies the associated cybersecurity challenges that might impact cloud-supported CV applications and corresponding solutions to these challenges. In addition, we present future research opportunities to prevent and mitigate cybersecurity issues in cloud computing for CV-related applications.

Due to the gradual increase in the volume of data generated by connected vehicles (CV), future vehicle-to-infrastructure (V2I) applications will require a communication medium that offers high-speed (high bandwidth) while maintaining reliability in high-mobility traffic scenarios. The 5G millimeter-wave (mmWave) can solve the communication issues related to V2I applications. However, the performance of the 5G mmWave for vehicular communication in high-mobility urban traffic scenarios is yet to be evaluated. This study presents a case study on assessing the performance of the 5G mmWave based vehicular communication in such traffic scenarios. We have designed three realistic use cases for performance evaluation based on three challenges: increased CV penetration level, dynamic mobility, and V2I application specifications, such as data rate and packet size. Then, we have created a simulation-based experimental setup using a microscopic traffic simulator (SUMO) and a communication network simulator (ns-3) to simulate the use cases. We have used delay, packet loss, throughput, and signal-to-interference-plus-noise ratio (SINR) as the communication performance evaluation metrics. Our analyses found that the CV penetration level is the primary determinant of the performance of the 5G mmWave. Moreover, once the data rate is increased by a factor of 40, delay and packet loss increase by factors of 6.8 and 2.8, respectively.

A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers. In a cloud-supported cyber-physical system environment, running a machine learning application in quantum computers is often difficult, due to the existing limitations of the current quantum devices. However, with the combination of quantum-classical neural networks (NN), complex and high-dimensional features can be extracted by the classical NN to a reduced but more informative feature space to be processed by the existing quantum computers. In this study, we developed a hybrid quantum classical NN to detect an amplitude shift cyber-attack on an in-vehicle controller area network (CAN) dataset. We showed that using the hybrid quantum-classical NN, it is possible to achieve an attack detection accuracy of 94%, which is higher than a Long short-term memory (LSTM) NN (87%) or quantum NN alone (62%)