A Hybrid CNN-LSTM Framework with Federated Learning for Enhanced Power Grid Intrusion Detection

Abstract

To address the issues of data silos, low detection accuracy, and insufficient generalization ability in traditional methods for power grid intrusion diagnosis, this study proposes the use of federated learning to construct a power grid intrusion diagnosis model and incorporates convolutional neural networks and long short-term memory network optimization models on this basis. The experiment outcomes indicate that in performance analysis, the accuracy of the raised model is 97.3%, the precision is 97.7%, the recall is 90.8%, the F1 value is 91.1%, the loss rate is 0.02, and the communication efficiency is 93.3%. In the case analysis, the error rate of the proposed model in dealing with Dos and Probe attacks does not exceed 1%, the storage value of abnormal intrusion information is 204 MB, the training time is 47.7 s, and the total expenditure required for the model in actual operation is the lowest. In summary, the raised model can substantially enhance the precision and timeliness of power grid intrusion diagnosis, and possesses significant practical utility, which can be widely applied in smart grid security systems.