基于Transformer的安卓恶意软件多分类模型

Android Malware Multiclassification Model Based on Transformer

由于开源性和开放性,安卓系统成为恶意软件攻击的热门目标,当前有大量针对安卓恶意软件检测的研究,其中机器学习算法得到广泛应用.通过对比在不同模型下将恶意软件转化为灰度图像和RGB图像的准确率,发现转化为RGB图像时恶意软件检测准确率更高,并使用自然语言处理中表现突出的Transformer算法对安卓软件classes.dex文件转换的RGB图像进行恶意软件多分类检测,结果发现与CNN,VGG等传统检测模型相比,使用基于Transformer的检测模型准确率更高.

Due to the open source and openness,the Android system has become a popular target for malware attacks,and there are currently a large number of research on Android malware detection,among which machine learning algorithms are widely used.In this paper,the Transformer algorithm is used to classify and detect the grayscale images converted by Android software classes.dex files,and the accuracy rate reaches 86%,which is higher than that of CNN,MLP and other models.