Migration Deep Learning Model for Malware Detection in Power Information Network Security

This paper proposes a deep learning-based approach to solve the malware classification problem. This method first uses image processing technology to convert the signature file of malware into a grayscale image. Then use, the convolutional neural network classifies the photos to achieve malware detection. First, this paper converts the feature file of the sample into a grayscale image and then uses the image classification method to judge the malware. This method is simple and easy, and image samples are more conducive to training deep-learning models. Secondly, this paper uses a convolutional neural network to solve image classification tasks. This model performs well in image processing and can automatically learn image features to improve classification accuracy. Finally, this paper uses data preprocessing techniques in critical steps, such as invalid data filtering and normalization, which can enhance the model’s generalization ability. The experimental results show that the method proposed in this paper achieves a high classification accuracy, and the loss and acc indicators also show sound convergence effects. This method can effectively detect malware and provide a guarantee for the security of the power information network. Further work will select a more appropriate model, increase the amount of data, optimize hyperparameters, and feature engineering to improve classification accuracy.