Critical Minority-Class Attack Detection for Industrial Internet Based on Improved Conditional Generative Adversarial Networks

Industrial-Internet security faces a core challenge: improving detection accuracy for critical minority-class network attacks. The existing intrusion detection methods based on Conditional Generative Adversarial Nets (CGANs) aim to achieve data balance by reconstructing minority-class attack samples. However, they encounter problems such as generating deceptive samples, poor sample quality, vanishing gradients and difficulties in training. This paper proposes an intrusion detection method based on the Multi-Discriminator Conditional Classification Generative Adversarial Network (MDCCGAN), an improved variant of CGAN, which integrates multiple discriminators and an independent classifier into the traditional CGAN framework. The multiple discriminators reduce the probability of generating deceptive samples, the independent classifier decouples the classification loss to clarify the direction of gradient updates, and the introduction of the Wasserstein distance fundamentally addresses the gradient-vanishing problem. Experiments conducted on the NSL-KDD and UNSW-NB15 datasets demonstrate that the proposed method significantly improves the recall, F1-score and accuracy for minority-class attacks. Specifically, on the NSL-KDD dataset, the overall accuracy increases from 74% to 94%, and the F1-score for the extremely rare U2R attack surges from 0% to 77%. Similarly, on the UNSW-NB15 dataset, the accuracy reaches 88%, a 10% improvement over the baseline DNN, and the F1-scores for extreme minority attacks such as Analysis, Backdoor, and Worms improved to 97%, 62%, and 84%, respectively. These results confirm that our method effectively outperforms traditional generation models and common class-balancing methods. It provides reliable technical support for industrial-Internet security.