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基于数据挖掘的恶意代码检测综述
Review of Malware Detection Based on Data Mining

查看参考文献40篇

文摘 数据挖掘是一种基于统计学的自动发掘数据规律的方法,它能通过分析海量样本的统计规律来建立判别模型,从而让攻击者难以掌握免杀的规律,近年来得到了广泛关注和快速发展。综述了数据挖掘技术应用于恶意代码检测领域所取得的研究成果;对所涉及的特征提取、特征选择、分类模型及其性能评估方法等方面的研究成果进行了深入分析和比较;最后提出了基于数据挖掘的恶意代码检测所面临的挑战,并对研究方向进行了展望。
其他语种文摘 Data mining is a method for automatically discovering data rule based on statistics which can analyze huge amounts of sample statistics to establish discriminative model,so that an attacker can not master the law to avoid detection. It has attracted widespread interests and has developed rapidly in recent years.In this paper,the research on malware detection based on data mining was summarized.The research results on feature extraction,feature selection,classification model and its performance evaluation methods were analyzed and compared in detail.At last,the challenges and prospect were provided in the field.
来源 计算机科学 ,2016,43(7):13-18,56 【扩展库】
DOI 10.11896/j.issn.1002-137X.2016.7.002
关键词 数据挖掘 ; 机器学习 ; 恶意代码检测 ; 特征提取 ; 特征选择
地址

沈阳理工大学信息科学与工程学院, 沈阳, 110159

语种 中文
文献类型 综述型
ISSN 1002-137X
学科 自动化技术、计算机技术
基金 国家自然科学基金
文献收藏号 CSCD:5784576

参考文献 共 40 共2页

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1 林杨东 恶意PDF文档检测技术研究进展 计算机应用研究,2018,35(8):2251-2255
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