IoT devices are highly vulnerable to cyberattacks due to their widespread, distributed nature and limited security features. Intrusion detection can counter these threats, but class imbalance between normal and abnormal traffic often degrades model performance. We propose a novel multi-generator adversarial data augmentation method that blends the strengths of TMG-GAN(Tabular Multi-Generator Generative Adversarial Network) and R3GAN (Re-GAN). Our approach uses multiple class-specific generators to create diverse, high-quality synthetic samples, improving training stability and minority-class detection. A dual-branch discriminator-classifier enhances authenticity and class prediction, while feature similarity and decoupling techniques ensure clear class separation. Experiments on TON-IoT and Edge-IIoTset datasets show our method outperforms existing techniques like hybrid sampling, SNGAN (Spectral Normalization GAN), and TMG-GAN, achieving higher detection accuracy and better minority-class recall for imbalanced IoT intrusion detection.
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