基于灰度图纹理指纹的恶意软件分类

计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 383-386.

基于灰度图纹理指纹的恶意软件分类

张晨斌,张云春,郑杨,张鹏程,林森

云南大学软件学院昆明650095

出版日期:2018-06-20 发布日期:2018-08-03
作者简介:张晨斌(1994-),男,主要研究方向为机器学习、网络安全;张云春(1981-),男,博士,讲师,主要研究方向为无线网络,E-mail:yunchunzhang@hotmail.com(通信作者);郑杨(1996-),男,主要研究方向为网络安全;张鹏程(1996-),男,主要研究方向为网络安全;林森(1994-),男,主要研究方向为机器学习。
基金资助:
云南省应用基础研究计划青年项目(2012FD004),国家自然科学基金项目(61363084,61363021),云南大学软件学院教育创新基金项目(2012EI07)资助

Malware Classification Based on Texture Fingerprint of Gray-scale Images

ZHANG Chen-bin,ZHANG Yun-chun, ZHENG Yang,ZHANG Peng-cheng, LIN Sen

School of Software,Yunnan University,Kunming 650095,China

Online:2018-06-20 Published:2018-08-03

摘要/Abstract

摘要： 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。

关键词: 恶意软件, 分类学习, 灰度图, 纹理特征

Abstract: 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.

Key words: Classification learning, Gray-scale images, Malwares, Texture feature

中图分类号:

TP399

张晨斌,张云春,郑杨,张鹏程,林森. 基于灰度图纹理指纹的恶意软件分类[J]. 计算机科学, 2018, 45(6A): 383-386. https://doi.org/

ZHANG Chen-bin,ZHANG Yun-chun, ZHENG Yang,ZHANG Peng-cheng, LIN Sen. Malware Classification Based on Texture Fingerprint of Gray-scale Images[J]. Computer Science, 2018, 45(6A): 383-386. https://doi.org/

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