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提升多维特征检测迷惑恶意代码
引用本文:孔德光,谭小彬,奚宏生,宫涛,帅建梅.提升多维特征检测迷惑恶意代码[J].软件学报,2011,22(3):522-533.
作者姓名:孔德光  谭小彬  奚宏生  宫涛  帅建梅
作者单位:1. 中国科学技术大学自动化系,安徽合肥,230027;Cyber-Security Laboratory,The Pennsylvania State University,University Park,State College,16801,USA
2. 中国科学技术大学自动化系,安徽合肥,230027
基金项目:国家高技术研究发展计划(863) (2006AA01Z449)
摘    要:针对迷惑恶意代码识别率较低的问题,提出一种基于提升多维特征的迷惑恶意代码检测算法.该算法在对迷惑恶意代码反汇编后进行静态分析,从Opcode分布序列,调用流图特征、系统调用序列图这3个特征维度对恶意代码家族特征进行归纳和分析,结合统计和语义结构特征表现恶意代码"行为"特性,从而对分类结果加权投票后给出迷惑恶意代码家族判...

关 键 词:恶意代码检测  多维特征  迷惑  提升
收稿时间:2009/3/19 0:00:00
修稿时间:6/1/2009 12:00:00 AM

Obfuscated Malware Detection Based on Boosting Multilevel Features
KONG De-Guang,TAN Xiao-Bin,XI Hong-Sheng,GONG Tao and SHUAI Jian-Mei.Obfuscated Malware Detection Based on Boosting Multilevel Features[J].Journal of Software,2011,22(3):522-533.
Authors:KONG De-Guang  TAN Xiao-Bin  XI Hong-Sheng  GONG Tao and SHUAI Jian-Mei
Affiliation:Department of Automation, University of Science and Technology of China, Hefei 230027, China; Cyber-Security Laboratory, The Pennsylvania State University, University Park, State College, 16801, USA;Department of Automation, University of Science and Technology of China, Hefei 230027, China;Department of Automation, University of Science and Technology of China, Hefei 230027, China;Department of Automation, University of Science and Technology of China, Hefei 230027, China;Department of Automation, University of Science and Technology of China, Hefei 230027, China
Abstract:To cope with the problem of the low accuracy in detecting obfuscated malware, an algorithm to detect obfuscated malware based on boosting multi-level features is presented. After a disassembly analysis and static analysis for the obfuscated malware, the algorithm extracts features from three dimensions: opcode distribution, a function call graph, and a system call graph, which combines the statistic and semantic features to reflect the behavior characteristic of the malware, and then gives out the decision result based on weighted voting for a different feature analysis. It has been proven by experiment that the algorithms have a much higher accuracy on the testing dataset.
Keywords:malware detection  multi-feature  obfuscate  boosting
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