GAN-augmented adversarial training for robust detection of complex jamming attacks in VANET intelligent systems

Noor Abdul Khaleq Zghair (), Wasnaa Kadhim Jawad (), Jinan Jawad Alwash (), Mohammed Amin Almaiah () and Rami Shehab ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 3, 4304-4314

Abstract: Vehicular Ad-hoc Networks (VANETs) are crucial for enabling real-time communication in intelligent transportation systems. While network availability and safety-critical services cannot be compromised under normal circumstances, they are highly vulnerable to jamming attacks. Jamming detection through traditional machine learning models is mainly static, and models are trained offline, making them prone to adaptation when facing various jamming patterns or adversarial updates. In this work, we introduce a robust and adaptive mechanism featuring Generative Adversarial Networks (GANs) for realistic attack simulation, a hybrid Convolutional Neural Network–Random Forest (CNN-RF) classifier for robust detection, and an online learning process with concept drift adaptation. Under the augmentation of the GAN module, adversarial and zero-day jamming behaviors are generated as new samples to expand the dataset; at the same time, the classifier is trained in an adversarial manner to ensure performance without damage under perturbation. Experimental results on a real-world DSRC vehicular intelligent dataset show that the proposed model achieves a 93.4% F1 score while reducing inference latency by 44.7% and attaining 92.3% model accuracy with over 10% drift—outperforming all other state-of-the-art baselines by a significant margin. These results demonstrate the potential of the model for real-time resilience and adversarial deployment within dynamic VANET settings.

Keywords: CNN-RF Hybrid Classifier; FGSM; Generative Adversarial Networks; Jamming Detection; Online Learning; PGD.VANET Security. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijirss.com/index.php/ijirss/article/view/7505/1600 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:3:p:4304-4314:id:7505

Access Statistics for this article

International Journal of Innovative Research and Scientific Studies is currently edited by Natalie Jean

More articles in International Journal of Innovative Research and Scientific Studies from Innovative Research Publishing
Bibliographic data for series maintained by Natalie Jean ().