Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)

Isra Mahmoudi, Djallel Eddine Boubiche (), Samir Athmani, Homero Toral-Cruz () and Freddy I. Chan-Puc
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Isra Mahmoudi: LEREESI Laboratory, HNS-RE2SD, Batna 05000, Algeria
Djallel Eddine Boubiche: LEREESI Laboratory, HNS-RE2SD, Batna 05000, Algeria
Samir Athmani: LEREESI Laboratory, HNS-RE2SD, Batna 05000, Algeria
Homero Toral-Cruz: Department of Sciences and Engineering, University of Quintana Roo, Chetumal 77019, Mexico
Freddy I. Chan-Puc: Department of Sciences and Engineering, University of Quintana Roo, Chetumal 77019, Mexico

Future Internet, 2025, vol. 17, issue 7, 1-40

Abstract: The increasing complexity and scale of Internet of Vehicles (IoV) networks pose significant security challenges, necessitating the development of advanced intrusion detection systems (IDS). Traditional IDS approaches, such as rule-based and signature-based methods, are often inadequate in detecting novel and sophisticated attacks due to their limited adaptability and dependency on predefined patterns. To overcome these limitations, machine learning (ML) and deep learning (DL)-based IDS have been introduced, offering better generalization and the ability to learn from data. However, these models can still struggle with zero-day attacks, require large volumes of labeled data, and may be vulnerable to adversarial examples. In response to these challenges, Generative AI-based IDS—leveraging models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers—have emerged as promising solutions that offer enhanced adaptability, synthetic data generation for training, and improved detection capabilities for evolving threats. This survey provides an overview of IoV architecture, vulnerabilities, and classical IDS techniques while focusing on the growing role of Generative AI in strengthening IoV security. It discusses the current landscape, highlights the key challenges, and outlines future research directions aimed at building more resilient and intelligent IDS for the IoV ecosystem.

Keywords: IoV; intrusion detection system; generative AI; machine learning; deep learning; transformers; GANs; VAEs (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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