Semantic lossless encoded image representation for malware classification

Combining artificial intelligence with static analysis is an effective method for classifying malicious code. Due to the development of anti-analysis techniques, malicious code commonly employs obfuscation methods like packing, which result in garbled assembly code and the loss of original semantics. Consequently, existing pre-trained code language models are rendered ineffective in such scenarios. Current research addresses this issue by converting malicious bytecode into grayscale images and extracting visual features for classification. However, this process truncates the original sequence, compromising its coherence and structure. Furthermore, the image dimensions undergo compression and cropping based on the model's input requirements, leading to the loss of intricate details. Our solution is a lossless encoding method for the visual structure of code, enabling unrestricted processing of malicious code images of any size. We convert bytecode files into semantically lossless images with proportional width. Then, we use image interleaving encoding to address semantic truncation issues caused by traditional image preprocessing methods. This method also prevents the loss of original code information due to image cropping or compression. For feature extraction, our goal is to combine the lossless encoding results with both local receptive field features and global contextual features. For local features, we achieve uniform embedding of variably sized input samples into equally sized feature maps using a multi-scale feature extraction module. For global contextual features, we reframe the feature maps along the row dimension, treating them as long-text sequences embedded in a matrix. We segment the feature maps into multiple row patch blocks and modify the Transformer's input components to cache and merge the hidden states of each block. Comparative experiments on various malware datasets demonstrate the effectiveness of our method, consistently achieving outstanding performance across classification metrics.