Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Area of Science:

• Computer Science
• Cybersecurity
• Network Engineering

Background:

• The Internet of Vehicles (IoV) is an extension of the Internet of Things (IoT), enabling vehicle-to-everything communication.
• IoV aims to improve driving, energy efficiency, data security, and road safety.
• Security and trust management are critical challenges in IoV environments.

Purpose of the Study:

• To provide an overview of IoV and trust management.
• To discuss security requirements, challenges, and attacks in IoV.
• To explore the potential of machine learning (ML) in addressing IoV security and trust issues.

Main Methods:

• Surveying existing literature on IoV security and trust management.
• Classifying machine learning techniques applicable to IoV.
• Reviewing machine learning-based security and trust management schemes for IoV.

Main Results:

• Identified key security and trust challenges in IoV environments.
• Classified various ML techniques relevant to IoV security.
• Highlighted the effectiveness of ML in enhancing IoV security and trust.

Conclusions:

• Machine learning presents a powerful approach to overcome IoV security and trust challenges.
• ML-based solutions are crucial for the future development of secure and reliable IoV systems.
• This survey provides a foundation for understanding and advancing ML applications in IoV security.