计算机工程与应用2025，Vol.61Issue(6)：317-327,11.DOI:10.3778/j.issn.1002-8331.2311-0219
融合CBAM的违法犯罪类安卓恶意软件检测与分类模型研究
Research on Detection and Classification Model of Illegal and Criminal Android Malware Inte-grating CBAM
摘要
Abstract
s:In response to the increasing frequency of illegal and criminal activities in mobile terminal APP in the field of public security work,a deep learning model based on the Android illegal and criminal APP dataset and integrating CBAM attention mechanism is proposed to address the issues of limited quantity and unclear classification of relevant datasets in the detection field of Android malicious illegal and criminal software,as well as the lack of feasible methods for identifying Android malicious and criminal software.Firstly,6 181 illegal and criminal APPs are collected and organized into 4 families.Grayscale,RGB,and RGBA images are performed in visualization processing on illegal APP software.A deep model fused with CBAM attention mechanism is used for family detection and classification.Experiments on the illegal and criminal APP dataset show that the Resnet18 model fused with CBAM mechanism improves its accuracy by 4.04%on RGBA images compared with grayscale images without the mechanism,reaching 93.52%.The fused CBAM mecha-nism model is validated on the public Drebin dataset,and the introduction of the CBAM deep learning model VGG16 achieves an accuracy of 96.35%on RGBA images.关键词
违法犯罪/安卓恶意软件/RGBA图像/可视化处理/卷积块注意力模块(CBAM)/深度学习Key words
illegal and criminal activities/Android malware/RGBA image/visualization processing/convolutional block attention module(CBAM)/deep learning分类
信息技术与安全科学引用本文复制引用
刘红玉,高见..融合CBAM的违法犯罪类安卓恶意软件检测与分类模型研究[J].计算机工程与应用,2025,61(6):317-327,11.基金项目
中国人民公安大学网络空间安全执法技术双一流创新研究专项(2023SYL07). （2023SYL07）
