AI ‐Induced Intrusion Detection for Photovoltaic Smart Grids With Adaptive Feature Learning Under 6G

The integration of photovoltaic (PV) smart grids with 6G networks advances sustainable energy systems but raises vulnerability to sophisticated cyber threats. Existing intrusion detection systems (IDS) suffer from static feature engineering, poor adaptability to dynamic 6G/PV environments, and high false positives, highlighting the need for a dynamically adaptive IDS. To address this, we propose an adaptive feature learning‐based IDS (AFL‐IDS) with three innovations: (i) mutual information‐driven cross‐domain fusion of PV operational and 6G traffic features; (ii) attention‐guided adaptive module for real‐time feature refinement; (iii) CNN–LSTM hybrid model with elastic weight consolidation to capture spatio‐temporal dependencies and avoid catastrophic forgetting. Evaluated on a 150 000‐instance dataset, AFL‐IDS outperforms state‐of‐the‐art baselines in key metrics. This confirms AFL‐IDS as a scalable, robust framework for mitigating cyber risks in 6G‐PV grids and laying a foundation for secure next‐generation smart energy systems.