计算机应用研究2017，Vol.34Issue(4)：1120-1123,1150,5.DOI:10.3969/j.issn.1001-3695.2017.04.037
恶意代码分类的一种高维特征融合分析方法
Analytical method of high-dimensional feature fusion for malware classification
摘要
Abstract
High-dimensional feature fusion and deep feature synthesis of malware features is new tendency and difficult problem of malware classification research.This paper presented a high-dimensional feature fusion method for malware classification.Firstly,it extracted features from both binary files and disassembly files using static analysis.Secondly,it analyzed and processed the high-dimensional feature vectors based on the SimHash method with the idea of locality-sensitive features.Finally,it trained and learned the fused feature vectors based on the classical machine learning method.Experimental results and analysis show that the proposed method is suitable for malware classification with high-dimensional features while only a small number of samples are available,and it can also improve the time performance of sample classification.关键词
恶意代码分类/特征提取/特征融合/深度特征处理/局部敏感哈希Key words
malware classification/feature extraction/feature fusion/deep feature synthesis/SimHash分类
信息技术与安全科学引用本文复制引用
崔弘,喻波,方莹..恶意代码分类的一种高维特征融合分析方法[J].计算机应用研究,2017,34(4):1120-1123,1150,5.基金项目
国家自然科学基金资助项目(61379148,61472437) （61379148,61472437）
