基于机器学习算法的主机恶意代码检测技术研究

doi:10.11959/j.issn.2096-109x.2017.00179

网络与信息安全学报 ›› 2017, Vol. 3 ›› Issue (7): 25-32.doi: 10.11959/j.issn.2096-109x.2017.00179

基于机器学习算法的主机恶意代码检测技术研究

张东,张尧,刘刚,宋桂香

浪潮电子信息产业股份有限公司，北京100085

修回日期:2017-07-02 出版日期:2017-07-01 发布日期:2017-08-01
作者简介:张东（1974-），男，山东威海人，浪潮电子信息产业股份有限公司高级工程师，主要研究方向为系统软件安全。|张尧（1988-），男，湖北襄阳人，博士，浪潮电子信息产业股份有限公司研究员，主要研究方向为网络安全、主机系统安全与应用密码学。|刘刚（1979-），男，四川德阳人，硕士，浪潮电子信息产业股份有限公司工程师，主要研究方向为操作系统安全、可信计算与云安全。|宋桂香（1978-），女，山东郓城人，浪潮电子信息产业股份有限公司工程师，主要研究方向为安全测评。

Research on host malcode detection using machine learning

Dong ZHANG,Yao ZHANG,Gang LIU,Gui-xiang SONG

Inspur Electronic Information Industry Co.,Ltd,Beijing 100085,China

Revised:2017-07-02 Online:2017-07-01 Published:2017-08-01

摘要/Abstract

摘要：

对机器学习算法下主机恶意代码检测的主流技术途径进行了研究，分别针对静态、动态这2种分析模式下的检测方案进行了讨论，涵盖了恶意代码样本采集、特征提取与选择、机器学习算法分类模型的建立等要点。对机器学习算法下恶意代码检测的未来工作与挑战进行了梳理。为下一代恶意代码检测技术的设计和优化提供了重要的参考。

关键词: 恶意代码检测, 机器学习, 静态分析, 动态分析, 分类模型

Abstract:

Main trends of host malcode detection using machine learning were focused on,and two categories of detection models(namely static analysis and dynamic analysis) were well discussed.Moreover,the critical stages such as malcode samples collection,feature extraction and selection,the construction of machine learning classifiers were considered fully.At last,some future work and challenges in this field were listed.The work can serve as a practical reference for establishing next-generation malcode detection techniques.

Key words: malcode detection, machine learning, static analysis, dynamic analysis, classification model

中图分类号:

TP309

张东,张尧,刘刚,宋桂香. 基于机器学习算法的主机恶意代码检测技术研究[J]. 网络与信息安全学报, 2017, 3(7): 25-32.

Dong ZHANG,Yao ZHANG,Gang LIU,Gui-xiang SONG. Research on host malcode detection using machine learning[J]. Chinese Journal of Network and Information Security, 2017, 3(7): 25-32.

图/表 4

参考文献 29

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基于机器学习算法的主机恶意代码检测技术研究

Research on host malcode detection using machine learning

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