为应对电力系统网络攻击检测面临的挑战,开发了一种基于深度学习的模型.该模型采用增强自适应弹性网络进行电力数据的特征提取,以增强数据的灵敏性并提高模型的训练和分类能力.此外,采用归一化和粒子群优化-K均值(PSO-K均值)噪声数据处理技术,以提高模型对噪声数据的适应性并缓解过拟合问题.采用基于CNN和LSTM的多层集成学习模型对噪声数据进行训练,从而提高分类器的准确性.在验证阶段,与K最近邻(KNN)、随机森林(RF)、支持向量机(SVM)、卷积神经网络(CNN)和其他模型相比,多层集成分类器表现出更优异的性能.值得注意的是,最佳分类器的准确率达到了 88.91%.该模型的有效性对于指导电力系统的稳定性和安全管理具有重要意义.
To address the challenges facing power system network attack detection,a deep learning-based model has been developed.The model employs an enhanced adaptive elastic network for the feature extraction of electric power data to enhance the sensitivity of the data and improve the model's training and classification capability.Additionally,normalization and particle swarm optimization-K-means(PSO-K-means)noise data processing techniques are employed to improve the model's adaptability to noise data and alleviate the over-fitting is-sue.A multi-layer ensemble learning model based on CNN and LSTM is utilized to train the noisy data,thereby increasing the classifier's accuracy.In comparison to K-nearest neighbor(KNN),random forest(RF),support vector machine(SVM),convolutional neural network(CNN),and other models during the verification phase,the multi-layer integrated classifier demonstrates superior performance.Notably,the accuracy of the best classifier reaches 88.91%.The model's effectiveness is significant for guiding power system stability and safety management.