Al‐Powered Intrusion Detection in Photovoltaic Smart Grids Over 6G Networks With Adaptive Feature Learning

ABSTRACT

The convergence of 6G communications and intelligent photovoltaic (PV) grids introduces severe cybersecurity risks, including sophisticated DDoS attacks, data manipulation, and unauthorized access, which conventional intrusion detection systems fail to adequately address in such dynamic and data‐intensive environments. This paper proposes a novel AI‐based intrusion detection framework tailored for 6G‐connected PV smart grids, with two core innovative modules: Multi‐modal Adaptive Variational Autoencoder (MA‐VAE) and Hierarchical Spatiotemporal Attention Network (HSTA‐Net). MA‐VAE features modality‐specific encoders, a shared latent space with disentangled representation learning and an adaptive weighting mechanism for dynamic adjustment of multi‐modal contribution weights in heterogeneous data fusion; HSTA‐Net integrates multi‐scale temporal convolution and gated attention, with a spatiotemporal attention mechanism focusing on key time points and feature dimensions for cross‐scale spatiotemporal dependency modeling. Experimental evaluations demonstrate that the proposed framework outperforms state‐of‐the‐art baseline methods in terms of detection accuracy and computational efficiency, achieving a 30% reduction in computational cost compared to recent large language model (LLM)‐based intrusion detection models. This work delivers a scalable, low‐latency security solution for 6G‐integrated PV grids and advances adaptive intrusion detection through effective multi‐source fusion and temporal modeling.