Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/7/310 |
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| author | Isra Mahmoudi Djallel Eddine Boubiche Samir Athmani Homero Toral-Cruz Freddy I. Chan-Puc |
| author_facet | Isra Mahmoudi Djallel Eddine Boubiche Samir Athmani Homero Toral-Cruz Freddy I. Chan-Puc |
| author_sort | Isra Mahmoudi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5fb1fb2d0c0e4e87a1d38f4a63e5e686 |
| institution | Matheson Library |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-5fb1fb2d0c0e4e87a1d38f4a63e5e6862025-07-25T13:23:45ZengMDPI AGFuture Internet1999-59032025-07-0117731010.3390/fi17070310Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)Isra Mahmoudi0Djallel Eddine Boubiche1Samir Athmani2Homero Toral-Cruz3Freddy I. Chan-Puc4LEREESI Laboratory, HNS-RE2SD, Batna 05000, AlgeriaLEREESI Laboratory, HNS-RE2SD, Batna 05000, AlgeriaLEREESI Laboratory, HNS-RE2SD, Batna 05000, AlgeriaDepartment of Sciences and Engineering, University of Quintana Roo, Chetumal 77019, MexicoDepartment of Sciences and Engineering, University of Quintana Roo, Chetumal 77019, MexicoThe 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.https://www.mdpi.com/1999-5903/17/7/310IoVintrusion detection systemgenerative AImachine learningdeep learningtransformers |
| spellingShingle | Isra Mahmoudi Djallel Eddine Boubiche Samir Athmani Homero Toral-Cruz Freddy I. Chan-Puc Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV) Future Internet IoV intrusion detection system generative AI machine learning deep learning transformers |
| title | Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV) |
| title_full | Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV) |
| title_fullStr | Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV) |
| title_full_unstemmed | Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV) |
| title_short | Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV) |
| title_sort | toward generative ai based intrusion detection systems for the internet of vehicles iov |
| topic | IoV intrusion detection system generative AI machine learning deep learning transformers |
| url | https://www.mdpi.com/1999-5903/17/7/310 |
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