SA-WGAN Based Data Enhancement Method for Industrial Internet Intrusion Detection

With the rapid development of the industrial Internet, the network security environment has become increasingly complex and variable. Intrusion detection, a core technology for ensuring the security of industrial control systems, faces the challenge of unbalanced data samples, particularly the low detection rates for minority class attack samples. Therefore, this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network (SA-WGAN) to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios. The proposed method integrates a self-attention mechanism with a Wasserstein Generative Adversarial Network (WGAN). The self-attention mechanism automatically learns important features from the input data and assigns different weights to emphasize the key features related to intrusion behaviors, providing strong guidance for subsequent data generation. The WGAN generates new data samples through adversarial training to expand the original dataset. In the SA-WGAN framework, the WGAN directs the data generation process based on the key features extracted by the self-attention mechanism, ensuring that the generated samples exhibit both diversity and similarity to real data. Experimental results demonstrate that the SA-WGAN-based data enhancement method significantly improves detection performance for attack samples from minority classes, addresses issues of insufficient data and category imbalance, and enhances the generalization ability and overall performance of the intrusion detection model.