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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The development of the intelligent transport space (ITS) comes with the challenge of securing transportation data. As the vehicular network is highly dynamic, the network architecture is susceptible to distributed malicious attacks irrespective of the emergence and integration of enabling technologies. A Software Defined Network (SDN) based VANET is an improvement over the traditional VANET and may be susceptible to attacks as a result of its centralized structure, resulting in dangerous circumstances. SDN-based VANET security for 5G deployments is essential and calls for integrating artificial intelligence tools for threat detection. An intrusion detection system was suggested in this work to detect Distributed Denial of Service (DDoS) attacks in the vehicular space. The proposed solution uses the Grid Search Cross-Validation (GSCV) exhaustive parameter search technique and the Radial Basis Function Kernel of the Support Vector Machine (RBF-SVM) algorithm. The performance of other ML algorithms implemented was compared using key performance metrics. Experimental simulations to validate the proposed framework's efficacy against DDoS intrusion show that the proposed scheme demonstrated an overall accuracy of 99.40% and a mean absolute error of 0.006. [ABSTRACT FROM AUTHOR]