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

Ridhi Jain, Norbert Tihanyi, Mohamed Amine Ferrag

TL;DR

This paper surveys the security landscape of integrating DL with IoV in smart cities, addressing data, model, and system vulnerabilities from adversarial, poisoning, and privacy perspectives. It reviews key DL paradigms (CNNs, RNNs, GANs, LLMs) in IoV applications and articulates their security implications, including the challenges of explainability and transfer-learning security. The authors propose multi-layer mitigation strategies—robustness, uncertainty estimation, privacy-preserving techniques, and robust IAM/secure updates—alongside future directions in edge computing, federated learning, and multi-modal data fusion. The work emphasizes the need for interdisciplinary collaboration, standards, and governance to realize safe, trustworthy, and scalable DL-enabled IoV ecosystems for resilient urban mobility.

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.

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

TL;DR

This paper surveys the security landscape of integrating DL with IoV in smart cities, addressing data, model, and system vulnerabilities from adversarial, poisoning, and privacy perspectives. It reviews key DL paradigms (CNNs, RNNs, GANs, LLMs) in IoV applications and articulates their security implications, including the challenges of explainability and transfer-learning security. The authors propose multi-layer mitigation strategies—robustness, uncertainty estimation, privacy-preserving techniques, and robust IAM/secure updates—alongside future directions in edge computing, federated learning, and multi-modal data fusion. The work emphasizes the need for interdisciplinary collaboration, standards, and governance to realize safe, trustworthy, and scalable DL-enabled IoV ecosystems for resilient urban mobility.

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.
Paper Structure (19 sections, 2 figures, 1 table)

This paper contains 19 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Deep learning in Internet of Vehicles
  4. Convolutional Neural Networks (CNNs) in Internet of Vehicles
  5. Recurrent Neural Networks (RNNs) in Internet of Vehicles
  6. Generative Adversarial Networks (GANs) in Internet of Vehicles
  7. Large Language Models (LLMs) in Internet of Vehicles
  8. Security Threats and Limitations
  9. Software Security in Deep Learning Technologies
  10. Data Security and Privacy
  11. Model Security
  12. Deep Learning-Specific Challenges
  13. System Security in Internet of Vehicles
  14. Network Security
  15. Identity and Access Management
  16. ...and 4 more sections

Figures (2)

  • Figure 1: Different types of communications in Internet of Vehicles (IoV)
  • Figure 2: Threats and opportunities in integrating Internet of Vehicles (IoV) with Deep Learning (DL)