计算机应用研究2018，Vol.35Issue(1)：261-265,5.DOI:10.3969/j.issn.1001-3695.2018.01.056
栈式自编码的恶意代码分类算法研究
Research on malicious code classification algorithm of stacked auto encoder
摘要
Abstract
Aiming at the traditional method can not effectively extract the potential characteristics of malicious code,this paper put forward stacked auto encoder to classify malicious code into families.Firstly,it studied and extracted the implicit features of the texture image and semantic in malicious code from a large number of training samples.In order to improve the accuracy of classification algorithm on the features selection,on the basis of that,it combined the implicit features of texture image and semantic in malicious code,to train stacked auto encoder and softmax regression.The experimental results demonstrate it that on the method of stacked auto encoder to classify malicious code into families,which is based on the implicit features of the texture image and semantic,it has better classification ability than traditional machine learning such as random forest,SVM.The accuracy rate is 2.474％ higher than the traditional random forest and is 1.235％ higher than SVM.关键词
栈式自编码/恶意代码/分类Key words
SAE(stacked auto encoder)/malicious code/classify分类
信息技术与安全科学引用本文复制引用
罗世奇,田生伟,孙华,禹龙..栈式自编码的恶意代码分类算法研究[J].计算机应用研究,2018,35(1):261-265,5.基金项目
新疆自治区科技人才培养项目(QN2016YX0051) （QN2016YX0051）
新疆自治区研究生科研创新资助项目(XJGRI2017007) （XJGRI2017007）
新疆自治区研究生教育创新计划科研创新项目(007号) （007号）
赛尔网络下一代互联网技术创新项目(NGII20170321) （NGII20170321）
