  Integrating Deep Learning (DL) techniques in the Internet of Vehicles (IoV)
introduces many security challenges and issues that require thorough
examination. This literature review delves into the inherent vulnerabilities
and risks associated with DL in IoV systems, shedding light on the multifaceted
nature of security threats. Through an extensive analysis of existing research,
we explore potential threats posed by DL algorithms, including adversarial
attacks, data privacy breaches, and model poisoning. Additionally, we
investigate the impact of DL on critical aspects of IoV security, such as
intrusion detection, anomaly detection, and secure communication protocols. Our
review emphasizes the complexities of ensuring the robustness, reliability, and
trustworthiness of DL-based IoV systems, given the dynamic and interconnected
nature of vehicular networks. Furthermore, we discuss the need for novel
security solutions tailored to address these challenges effectively and enhance
the security posture of DL-enabled IoV environments. By offering insights into
these critical issues, this chapter aims to stimulate further research,
innovation, and collaboration in securing DL techniques within the context of
the IoV, thereby fostering a safer and more resilient future for vehicular
communication and connectivity.
