A Diffusion Model Approach to Malicious Code Dataset Expansion
LI Sicong
WANG Jian
SONG Yafei
WANG Shuo
FENG Cunqian
Abstract：With the support of big data in recent years,deep learning models have been demonstrating excellent capabilities in the aspects of computer vision and natural language processing.However,in the application of mali-cious code images fields,it is entirely possible for the malicious code to be insufficient training data.The distribu-tion of whole dataset with number of training samples in some malicious families being limited is hardly character-ized fully,and the deep learning model may be over-fitted to these scarce data,resulting in poor model perform-ance.In view of the above-mentioned problems,this paper proposes a dataset expansion method based on the dif-fusion model to generate new samples.Such a method is to achieve dataset expansion by learning the conversion process from the original data to noise and using the inverse process to reduce the noise samples into new similar samples,generating new samples similar to the original dataset but different from the original dataset,alleviating the impact of the imbalance of data of some of the families on the classification and detection task,and improving the model's generalization ability.
Keywords：malicious code detectiondiffusion modelsmalicious code visualizationdata enhancement tech-niquesU-Net
Publication Date：2025-02-24
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 95-103 )