Causal Grey Wolf Optimization: A Novel Approach to Robust Network Anomaly Detection Amidst Data Redundancy

The rapid proliferation of IoT technologies intensifies network anomaly detection challenges because of high-dimensional and redundant data. To address this issue, we propose causal gray wolf optimization (CGWO), a framework that integrates the global search capabilities of gray wolf optimization (GWO) with causal inference to distinguish causal features from spurious correlations. CGWO employs a four-stage process: (1) data preprocessing with causal weight transformation, (2) causal analysis via potential outcome models (POMs), (3) feature subset optimization using a fitness function balancing accuracy and dimensionality, and (4) anomaly detection with an enhanced convolutional autoencoder (ECAE). By prioritizing causal relationships over statistical correlations, CGWO minimizes redundant features while increasing model interpretability. When evaluated with benchmark datasets (NSL-KDD and UNSW-NB15), CGWO achieves 99.8% accuracy (14 features) for 5-class classification with NSL-KDD and 94.2% accuracy (10 features) for 10-class classification with UNSW-NB15, outperforming conventional methods by more than 6%. The framework reduces feature dimensions by 65–80% without performance loss, demonstrating robustness, computational efficiency (120–150 s per dataset), and scalability for edge computing. These results validate the effectiveness of CGWO in balancing dimensionality reduction and interpretable model design for complex network environments.