Guest Editors
Dr. Maha Driss, Prince Sultan University, Saudi Arabia.
Dr. Wadii Boulila, Prince Sultan University, Saudi Arabia.
Dr. Jawad Ahmad, Edinburgh Napier University, UK.
Summary
The Internet of Vehicles (IoV) is developing as a new emergent paradigm, thanks to the rapid proliferation of wireless and mobile communication technologies, as well as recent improvements in cloud computing services. The IoV is regarded as a major societal application for providing prompt and effective transmission of information such as vehicle speed, position, and route information to ensure smart driving, prevent road accidents, increase traffic efficiency, reduce fuel consumption, and deliver location-based services. The growth of machine-enabled cognition via Machine Learning (ML), Artificial Intelligence (AI), and associated technologies has accelerated the technical evolution of transportation systems one step further, resulting in a new framework called the Cognitive Internet of Vehicles (C-IoV). When compared to its predecessor, the redefined cognitive technology in C-IoV guarantees significant enhancements and optimized network capacities, enabling technical and decision-making support to ensure error-free driving, timely emergency response, and advanced driving assistance. However, cybersecurity and privacy issues represent the most significant barriers to the future acceptance and development of C-IoV-based transportation systems. Indeed, if there are network breaches in C-IoV, the vehicles may be controlled by malevolent hackers, resulting in traffic accidents. Besides, because collected sensitive data is processed and analyzed utilizing cloud services, this will lead to the leakage of data privacy.
This special issue welcomes high-quality research contributions in the topic of Cognitive Internet of Vehicles security and privacy from both academia and industry. Experimental results or literature reviews on security and privacy challenges and solutions in the Cognitive Internet of Vehicles are encouraged to be submitted.
Keywords
• Evaluation of security and privacy vulnerabilities and threats in C-IoV ecosystems
• Novel attacks and related countermeasures
• Novel privacy-preserving solutions in C-IoV ecosystems
• Lightweight security models for the C-IoV
• Efficient encryption methods and algorithms for securing C-IoV ecosystems
• Machine learning algorithms for securing C-IoV ecosystems
• Deep learning algorithms for securing C-IoV ecosystems
• Reinforcement learning-based intelligent applications in C-IoV
• Federated learning-based intelligent applications in C-IoV
• Big data analytics for cybersecurity in C-IoV
• Intrusion Detection Systems for anomaly/malware detection and classification in C-IoV
• Blockchain-based security solutions for C-IoV ecosystems
• Securing 5G and 6G-based communication in C-IoV ecosystems
• Enhancing access control with improved authentication techniques in C-IoV ecosystems
• Security and privacy in C-IoV-based cloud/fog/edge computing applications
• Optimized low power cybersecurity and privacy preservation techniques
• for effective C-IoV-based applications
• Current cybersecurity practices and future perspectives for sustainable C-IoV systems
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