通信学报2025，Vol.46Issue(3)：187-198,12.DOI:10.11959/j.issn.1000-436x.2025035
基于图像域的轻量级恶意软件分类方法研究
Research on lightweight malware classification method based on image domain
摘要
Abstract
To address the high deployment costs and long prediction times associated with traditional malware classifica-tion methods,a lightweight malware visualization classification method was proposed.Firstly,a CBG algorithm was in-troduced to solve the problems of imbalanced image sizes and excessive noise in malware images.Then,to capture fea-ture 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 con-volution 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.关键词
恶意软件分类/图像增强/轻量级模型/轻量通道注意力Key words
malware classification/image enhancement/lightweight model/lightweight channel attention分类
计算机与自动化引用本文复制引用
孙敬张,程轶男,邹炳慧,乔彤华,符思政,张琪,曹春杰..基于图像域的轻量级恶意软件分类方法研究[J].通信学报,2025,46(3):187-198,12.基金项目
海南省科技人才创新基金资助项目(No.KJRC2023B13,No.KJRC2023D30) Hainan Province Science and Technology Talents Innovation Project(No.KJRC2023B13,No.KJRC2023D30) （No.KJRC2023B13,No.KJRC2023D30）
