基于自注意力与多模态融合的电力系统攻防协同模型OA

[Objective]Aiming at the problems of adversarial attack risks and insufficient offensive-defense coordination of data-driven algorithms in new power systems,a theoretical framework for co-optimization of adversarial attacks and defense is established.This framework aims to enhance attack targeting,defense robustness,and the capability to identify complex attack features,thereby establishing a closed-loop optimization mechanism for offensive-defense co-evolution.[Methods]In the adversarial attack generation module,a self-attention mechanism is utilized to quantify node feature contributions,and a Top-K strategy is combined to screen key nodes.An encoder-decoder architecture and reinforcement learning are employed to dynamically optimize perturbation strategies,and a filter retains perturbations on key nodes to improve attack efficiency.In the adversarial defense model,a stacked autoencoder extracts static structural features,while a convolutional neural network-long short-term memory network fuses spatiotemporal features.These multi-modal features are then integrated via a dynamic weighting strategy and fed into a support vector machine classifier to distinguish attack samples from normal samples.[Results]Compared with random node attacks,the fast gradient sign method,and projected gradient descent attacks,the proposed attack method maintains a high success rate while demonstrating robustness across the entire attack intensity range that better aligns with the practical requirements of power system adversarial attacks.Furthermore,perturbations can be concentrated on key nodes,verifying the advantage of attack targeting.On the defense side,the fusion model's performance significantly surpasses that of single models,highlighting the strong identification capability of multi-modal feature fusion for complex attack patterns.[Conclusions]On the attack side,the integration of self-attention and reinforcement learning achieves targeted perturbation on key nodes.On the defense side,the adoption of multi-modal feature fusion enhances the identification capability for complex attacks.Furthermore,a dynamic co-evolution of offensive and defensive strategies is realized through a closed-loop feedback mechanism.