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 Integrating CBAM

作　　者：刘红玉 高见[1] LIU Hongyu;GAO Jian(School of Cybersecurity,People’s Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学信息网络安全学院,北京100038

出　　处：《计算机工程与应用》2025年第6期317-327,共11页Computer Engineering and Applications

基　　金：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)。

摘　　要：针对公安工作领域移动终端APP违法犯罪日益频发的情况,为解决Android恶意违法犯罪软件检测领域中相关数据集数量少、分类不清晰,识别Android恶违法软件可行性方法匮乏等情况,提出了一种基于安卓违法犯罪APP数据集,融合CBAM注意力机制的深度学习模型。收集6181个违法犯罪类APP并整理划分为4个家族;对违法APP软件进行灰度图、RGB以及RGBA三种图像可视化处理;利用融合CBAM注意力机制的深度模型进行家族检测分类。在违法犯罪APP数据集上的实验表明,融合CBAM机制的Resnet18模型在RGBA图像上与未引入该机制的灰度图图像相比,准确度提升了4.04%,达到93.52%。融合CBAM机制的模型在公开Drebin数据集上进行了验证,引入CBAM深度学习模型VGG16在RGBA图像上取得了96.35%的准确率。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,6181 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 mechanism 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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]