The Metaverse is a concept used to refer to a virtual world that exists in parallel to the physical world. It has grown from a conceptual level to having real applications in virtual reality games. The applicability of the Metaverse in numerous sectors like marketing, education, social, and even advertising exists. However, there exists little or no work on Metaverse applicability to the transportation industry. Data‐driven intelligent transportation systems (DDITS) aim to provide more intelligent systems based on exploiting data. This paper reviews the concepts and features of the Metaverse. Also, the review goes over three dominant DDITS challenges: vehicle fault detection and repair, testing new technologies, and anti‐theft systems. In addition, it highlights prospective Metaverse solutions that apply to the DDITS. Buttressing the utility of Metaverse in DDITS, this paper presents two major case studies: the invisible to visible (I2V) and the Metaverse on Wheels (MoW) technologies. Finally, the influence, limitations, and open issues of Metaverse applications to DDITS are discussed.

The potential for an intelligent transportation system (ITS) has been made possible by the growth of the Internet of things (IoT) and artificial intelligence (AI), resulting in the integration of IoT and ITS—known as the Internet of vehicles (IoV). To achieve the goal of automatic driving and efficient mobility, IoV is now combined with modern communication technologies (such as 5G) to achieve intelligent connected vehicles (ICVs). However, IoV is challenged with security risks in the following five (5) domains: ICV security, intelligent device security, service platform security, V2X communication security, and data security. Numerous AI models have been developed to mitigate the impact of intrusion threats on ICVs. On the other hand, the rise in explainable AI (XAI) results from the requirement to inject confidence, transparency, and repeatability into the development of AI for the security of ICV and to provide a safe ITS. As a result, the scope of this review covered the XAI models used in ICV intrusion detection systems (IDSs), their taxonomies, and outstanding research problems. The results of the study show that XAI though in its infancy of application to ICV, is a promising research direction in the quest for improving the network efficiency of ICVs. The paper further reveals that XAI increased transparency will foster its acceptability in the automobile industry.

The dynamic nature of the vehicular space exposes it to distributed malicious attacks irrespective of the integration of enabling technologies. The software-defined network (SDN) represents one of these enabling technologies, providing an integrated improvement over the traditional vehicular ad-hoc network (VANET). Due to the centralized characteristics of SDN, they are vulnerable to attacks that may result in life-threatening situations. Securing SDN-based VANETs is vital and requires incorporating artificial intelligence (AI) techniques. Hence, this work proposed an intrusion detection model (IDM) to identify Distributed Denial-of-Service (DDoS) attacks in the vehicular space. The proposed solution employs the radial basis function (RBF) kernel of the support vector machine (SVM) classifier and an exhaustive parameter search technique called grid search cross-validation (GSCV). In this framework, the proposed architecture can be deployed on the onboard units (OBUs) of each vehicle, which receive the vehicular data and run intrusion detection tasks to classify a message sequence as a DDoS attack or benign. The performance of the proposed algorithm compared to other ML algorithms using key performance metrics. The proposed framework is validated through experimental simulations to demonstrate its effectiveness in detecting DDoS intrusion. Using the GridSearchCV, optimal values of the RBF-SVM kernel parameters “C” and “gamma” $(\gamma)$ of 100 and 0.1, respectively, gave the optimal performance. The proposed scheme showed an overall accuracy of 99.33%, a detection rate of 99.22%, and an average squared error of 0.007, outperforming existing benchmarks.

This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper’s structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT.