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Title	Securing Tomorrow’S Smart Cities: Investigating Software Security In Internet Of Vehicles And Deep Learning Technologies
ID_Doc	47813
Authors	Jain R.; Tihanyi N.; Ferrag M.A.
Year	2025
Published	Lecture Notes in Intelligent Transportation and Infrastructure, Part F99
DOI	http://dx.doi.org/10.1007/978-3-031-72959-1_16
Abstract	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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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