基于灰度图纹理指纹的恶意软件分类

随着安卓恶意软件数量的快速增长,传统的恶意软件检测与分类机制存在检测率低、训练模型复杂度高等问题。为解决上述问题,结合图像纹理特征提取技术和机器学习分类器,提出基于灰度图纹理特征的恶意软件分类方法。该方法首先将恶意软件样本生成灰度图,设计并集成了包含GIST和Tamura特征提取算法在内的4种特征提取方法；然后将所得纹理特征集合作为源数据,基于Caffe高性能处理架构构造了5种分类学习模型,最终实现对恶意软件的检测和分类。实验结果表明,基于图像纹理特征的恶意软件分类具有较高的准确率,且Caffe架构能有效缩短学习时间,降低复杂度。

Malware Classification Based on Texture Fingerprint of Gray-scale Images

With the rapid increment of the number of Android malwares,the traditional malware detection and classification methods were proved to be with low detection rate,highly complex training model and so on.To solve above problems,the texture feature of gray-scale image-based malware classification method was proposed by combining the image texture feature abstraction and machine learning classifiers.The proposed method starts with converting the malware samples into grayscale images.Four feature abstraction methods were designed including GIST and Tamura-based feature abstraction algorithm.By taking the texture feature as the source data,5 kinds of classification learning models were constructed by using high performance architecture Caffe.Finally,the detection and classification of malwares were done.The experimental results show that the image texture feature-based malware classification achieves high accuracy,and the Caffe architecture can effectively improve the learning time which further reduces the complexity.