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基于聚类分析的内核恶意软件特征选择

陈志锋 李清宝 张平 冯培钧

陈志锋, 李清宝, 张平, 冯培钧. 基于聚类分析的内核恶意软件特征选择[J]. 电子与信息学报, 2015, 37(12): 2821-2829. doi: 10.11999/JEIT150387
引用本文: 陈志锋, 李清宝, 张平, 冯培钧. 基于聚类分析的内核恶意软件特征选择[J]. 电子与信息学报, 2015, 37(12): 2821-2829. doi: 10.11999/JEIT150387
Chen Zhi-feng, Li Qing-bao, Zhang Ping, Feng Pei-jun. Signature Selection for Kernel Malware Based on Cluster Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2821-2829. doi: 10.11999/JEIT150387
Citation: Chen Zhi-feng, Li Qing-bao, Zhang Ping, Feng Pei-jun. Signature Selection for Kernel Malware Based on Cluster Analysis[J]. Journal of Electronics & Information Technology, 2015, 37(12): 2821-2829. doi: 10.11999/JEIT150387

基于聚类分析的内核恶意软件特征选择

doi: 10.11999/JEIT150387
基金项目: 

核高基国家科技重大专项(2013JH00103)和国家863计划目标导向项目(2009AA01Z434)

Signature Selection for Kernel Malware Based on Cluster Analysis

Funds: 

The National Science and Technology Major Project of China (2013JH00103)

  • 摘要: 针对现有基于数据特征的内核恶意软件检测方法存在随特征的增多效率较低的问题,该文提出一种基于层次聚类的特征选择方法。首先,分析相似度计算方法应用于数据特征相似度计算时存在的困难,提出最长公共子集并设计两轮Hash求解法计算最长公共子集;其次,设计基于最长公共子集的层次聚类算法,有效地将相似特征聚类成簇;在此基础上,设计基于不一致系数的内核恶意软件特征选择算法,大大减少特征数,提高检测效率。实验结果验证了方法的有效性,且时间开销在可接受的范围内。
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出版历程
  • 收稿日期:  2015-04-02
  • 修回日期:  2015-07-30
  • 刊出日期:  2015-12-19

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