{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T19:55:58Z","timestamp":1780948558990,"version":"3.54.1"},"reference-count":93,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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\u2014leveraging models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers\u2014have 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.<\/jats:p>","DOI":"10.3390\/fi17070310","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T13:00:51Z","timestamp":1752757251000},"page":"310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)"],"prefix":"10.3390","volume":"17","author":[{"given":"Isra","family":"Mahmoudi","sequence":"first","affiliation":[{"name":"LEREESI Laboratory, HNS-RE2SD, Batna 05000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1566-3005","authenticated-orcid":false,"given":"Djallel Eddine","family":"Boubiche","sequence":"additional","affiliation":[{"name":"LEREESI Laboratory, HNS-RE2SD, Batna 05000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samir","family":"Athmani","sequence":"additional","affiliation":[{"name":"LEREESI Laboratory, HNS-RE2SD, Batna 05000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4421-3775","authenticated-orcid":false,"given":"Homero","family":"Toral-Cruz","sequence":"additional","affiliation":[{"name":"Department of Sciences and Engineering, University of Quintana Roo, Chetumal 77019, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Freddy I.","family":"Chan-Puc","sequence":"additional","affiliation":[{"name":"Department of Sciences and Engineering, University of Quintana Roo, Chetumal 77019, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/MCE.2021.3137790","article-title":"AI-Based Intrusion Detection for Intelligence Internet of Vehicles","volume":"12","author":"Man","year":"2021","journal-title":"IEEE Consum. 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