Fine-grained Classification of Malicious Code Based on CNN and Multi-resolution Feature Fusion

With the development of the Internet, security issues in the network have attracted more and more attention. Variants of malicious code are constantly increasing, and their attacks will have a serious impact on the network environment, so effective detection of malicious code has important research significance. However, the current malicious code detection methods still have some problems, such as code detection, cumbersome feature extraction, and misclassification between similar families. To this end, the paper proposes a fine-grained detection method for malicious code. First visualized the binary files of malicious code and converted them into grayscale images. Then, use the improved convolutional neural network to extract the multi-resolution features of grayscale images, and use the interactive fusion method to fuse these features. Finally, input the fused features into the fully connected layer to complete the fine-grained classification of malicious code. Experiments prove that our method is indeed effective for fine-grained classification of malicious code.