Research on lightweight malware classification method based on image domain

To address the high deployment costs and long prediction times associated with traditional malware classification methods, a lightweight malware visualization classification method was proposed. Firstly, a CBG algorithm was introduced to solve the problems of imbalanced image sizes and excessive noise in malware images. Then, to capture feature relationships effectively and reduce computational complexity, a lightweight channel attention mechanism was implemented. This mechanism guided the model to focus on more informative features, while depthwise separable convolution further reduced the number of model parameters. Experimental results on three large malware datasets, MalImg, BIG2015, and BODMAS, demonstrate that the proposed model achieved classification accuracies of 99.68%, 99.45%, and 93.12%, with model sizes of 442 KB, 414 KB, and 423 KB, and prediction times of 14.12 ms, 11.09 ms, and 4.11 ms per image, respectively. This method demonstrates state-of-the-art performance in accuracy, model size, and inference speed.