计算机与现代化Issue(7)：28-32,42,68,7.DOI:10.3969/j.issn.1006-2475.2025.07.005
基于RA-CNN与ResNet的安卓恶意应用检测
Android Malicious Application Detection Based on RA-CNN and Residual Network
摘要
Abstract
In recent years,Android malware detection methods based on bytecode images and deep learning have become in-creasingly popular,but such methods have the problems of limited feature extraction and sensitivity to noise data.To solve these problems,this paper proposes a detection method of fusion Residual Network(ResNet)and Recursive Attention Network(RACNN).In this method,three bytecode files of DEX,XML and ARSC are extracted from the software samples and mapped to RGB images,and then the convolutional neural network embedded in the residual structure is used for feature abstraction and ex-traction.Subsequently,the Attention Suggestion Sub-Network(APN)uses the feature map as a reference to iteratively generate local region attention from coarse to fine.Meanwhile,the finer scale network magnifies the region of interest from the previous scale as the input of the next scale in a cyclic manner,and realizes classification through multi-scale learning.Experiments show that compared with similar bytecode-based image methods,the proposed method has improved in some indicators,the accuracy reaches 98.28%.关键词
递归注意力网络/残差网络/XML文件/ARSC文件/字节码图像Key words
recurrent attention network/residual network/XML file/ARSC file/bytecode image分类
信息技术与安全科学引用本文复制引用
华漫,刘小亮..基于RA-CNN与ResNet的安卓恶意应用检测[J].计算机与现代化,2025,(7):28-32,42,68,7.基金项目
四川科技厅重点研发项目(2023YFG0171) （2023YFG0171）
中央高校基本科研业务费重点项目(24CAFUC01009) （24CAFUC01009）
