An Intrusion Detection Method Based on Dynamic Social Structure Gray Wolf Optimization and Multi-Scale Temporal Perception

The dispatching data network and information management system in smart grids constitute a critical communication infrastructure that requires continuous security monitoring. However, network attacks exhibit multi-scale temporal characteristics ranging from microsecond-level bursts to slow intrusions lasting minutes, making single-scale detection models insufficient. Moreover, the hyperparameter space of deep learning models is large and highly non-convex, rendering traditional manual tuning inefficient. To address these challenges, this paper proposes an intrusion detection method based on the Dynamic Social Gray Wolf Optimizer (DSGWO) and the Multi-Scale Temporal Convolutional Network (MSTCN). The DSGWO maintains population diversity via an underdog alliance and breaks elite monopoly through a rank promotion challenge mechanism, balancing exploration and exploitation to avoid premature convergence. The MSTCN employs multi-scale parallel branches whose key training hyperparameters are optimized by the DSGWO, with residual connections and feature fusion for robust temporal modeling. Experiments on UNSW-NB15 and CIC-IDS-2017 demonstrate that DSGWO-MSTCN achieves F1-scores of 0.9933 and 0.9892, respectively, outperforming GWO, PSO, and NGO-based optimization approaches.