MCSMGS:Malware Classification Model Based on Deep Learning

摘要：

　　As a major threat to cyber security,malware has been increasingly damaging national security.This paper proposes a malware classification model,i.e.MCSMGS model(Malware Classification Based on Static Malware Gene Sequences),that combines the static malware genes with deep learning methods.The model extracts the malware gene sequences that have both material attribute and informational attribute.Then it makes distributed representation for each malware gene to represent the intrinsic correlation and similarity.Finally,the SMGS_CNN(Static Malware Gene Sequences--Convolution Neural Network)module is used to construct the neural network to analyze the malware gene sequences and realize malware classification.The experimental results show that the classification accuracy is greatly improved and up to 98%with the MCSMGS model.CNN model is more effective than the traditional SVM model.

关键词： malware gene sequence intrinsic correlation similarity neural network classification

作者: Xi Meng Zhen Shan Fudong Liu Bingling Zhao Jin Han Jing Wang Hongyan Wang

作者单位: State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou,China Division of Information Theory Aviation University of Air Force Changchun,China

会议类型: 国际会议

会议名称: 第九届网络分布式计算与知识发现国际会议( 2017 International Conference on Cyber-enabled distributed computing and knowledge discovery)

会议地点: 南京

会议语种:英文

页码: 272-275

在线出版日期: 2017-10-12（万方平台首次上网日期，不代表论文的发表时间）