Securing Tomorrow's Smart Cities: Investigating Software Security in Internet of Vehicles and Deep Learning Technologies

TL;DR

This paper reviews security challenges and vulnerabilities in integrating Deep Learning within Internet of Vehicles systems, highlighting threats like adversarial attacks and data breaches, and emphasizing the need for tailored security solutions.

Contribution

It provides a comprehensive analysis of security issues in DL-enabled IoV systems and discusses potential solutions to enhance their robustness and trustworthiness.

Findings

DL introduces significant security vulnerabilities in IoV.

Adversarial attacks threaten the integrity of DL models in IoV.

Enhanced security protocols are needed for safe DL deployment in IoV.

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.