基于Swin-Transformer的可视化安卓恶意软件检测研究
Research on Visual Android Malware Detection Based on Swin-Transformer
王海宽 1原锦明1
作者信息
- 1. 晋城职业技术学院信息工程系,山西晋城 048026
- 折叠
摘要
为了更好地利用深度学习框架防范安卓平台上恶意软件攻击,提出了一种新的应用程序可视化方法,从而弥补了传统的采样方法存在的信息损失问题;同时,为得到更加准确的软件表示向量,使用了 Swin-Transformer 架构代替传统的卷积神经网络(CNN:Convolutional Neural Network)架构作为特征提取的主干网络.实验采用的数据集中的样本来自Drebin与CICMalDroid 2020数据集.研究结果表明,新提出的可视化方法优于传统的可视化方法,检测系统的准确率达到97.39％,具有较高的恶意软件识别能力.
Abstract
The connection between mobile internet devices based on the Android platform and people's lives is becoming increasingly close,and the security issues of mobile devices have become a major research hotspot.Currently,many visual Android malware detection methods based on convolutional neural networks have been proposed and have shown good performance.In order to better utilize deep learning frameworks to prevent malicious software attacks on the Android platform,a new application visualization method is proposed,which to some extent compensates for the information loss problem caused by traditional sampling methods.In order to obtain more accurate software representation vectors,this study uses the Swin Transformer architecture instead of the traditional CNN(Convolutional Neural Network)architecture as the backbone network for feature extraction.The samples used in the research experiment are from the Drebin and CICCalDroid 2020 datasets.The research experimental results show that the proposed visualization method is superior to traditional visualization methods,and the detection system can achieve an accuracy of 97.39％,with a high ability to identify malicious software.
关键词
安卓恶意软件/深度学习/计算机视觉Key words
Android malware/deep learning/computer vision引用本文复制引用
基金项目
山西省教育科学规划基金(十四五)(GH-221026)
晋城职业技术学院校级基金(LX2216)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据