[Retracted] Deep and Reinforcement Learning Technologies on Internet of Vehicle (IoV) Applications: Current Issues and Future Trends

Table 1

Summary of deep learning applications in secure internet of vehicles.

Year	Source	Security approaches	Features	Advantages	Citations
2022	Elsevier	Trust management in vehicular environments	Enhances the users’ quality of experience	A comprehensive survey on trust management for IoV	Hbaieb et al. [39]
2021	ArXiv	Cybersecurity in cognitive IoV (CIoV)	Cognitive design mechanisms to avoid potential security issues	Technical concepts about security, privacy, and trust in CIoV	Hasan et al. [40]
2021	IJSSE	Intrusion prevention system (IPS) for cognitive IoV (CIoV)	Malicious packet identification	Higher accuracy closed to 99.57%	Praneeth et al. [41]
2021	Hindawi	Secured IoV communication	Improves IoV edge computing offloading model	Reviewing the most recent AI approaches used in IoV security	Sayed Ali et al. [42]
2020	IEEE	Blockchain-enabled IoV framework	Improves vehicular security	Efficient accuracy of security	Yanxing et al. [43]
2020	Springer	Location privacy protection in mobile edge computing IoV	Deep reinforcement learning to secure MEC servers	Highest secured computing task and effective scheduling strategy	Pang et al. [44]
2020	Computers, MDPI	Blockchain base IoV design for security issues	Evaluates different AI algorithms	Out performance of IoV cybersecurity solutions	Mecheva and Kakanakov [45]
2019	Springer	Security against intrusion attacks	DL methods for validity and security threats	Secure observed data stream and Internet connectivity	Berger et al. [46]
2018	IEEE	Detection of cyberattacks in IoV	DL and NN for different attack learning	Efficient detection latency against attacks	Loukas et al. [43]
2018	IEEE	Smart spectrum utilization (SSU) with IoV	Machine learning for secure IoV	Secure IoV applications and services	Ali et al. [47]