Federated Learning Malware Detection Method Based on Voting Mechanism:Taking Power Grid as An Example
To protect user privacy of the secondary units in power grid enterprises and increase the virus detection capability of distributed clients,the federated learning mechanism was applied to the malicious code detection task.In this framework,the global model parameters were calculated by using the local model.A federal learning malicious code detection method based on voting mechanism was proposed.In the communication process of the device,the original data of the node was not sent,but the model parameters were sent,which effectively protected the data privacy of each device.By randomly selecting users to partici-pate in the voting,the control center can adjust the hyperparameters of the local and global models based on the voting results.Finally,the local model parameters were aggregated by weighted aggregation to obtain a high-precision global malicious code classification model.The model provided privacy protection while maintaining a high malicious code detection accuracy.Experi-ments show that the classification accuracy of this method on multiple malicious code datasets is improved,and the loss function value of the model is reduced.
万方数据
维普
