Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (4): 309-315.doi: 10.11896/jsjkx.201100171

• Information Security • Previous Articles     Next Articles

Study on Malicious Behavior Graph Construction and Matching Algorithm

WANG Le-le1, WANG Bin-qiang1, LIU Jian-gang2, MIAO Qi-guang3   

  1. 1 Institute of Information Technology,Information Engineering University,Zhengzhou 450000,China
    2 Nanjing Information Technology Institute,Nanjing 210000,China
    3 School of Computer Science and Technology,Xidian University,Xi’an 710071,China
  • Received:2020-06-24 Revised:2021-02-04 Online:2021-04-15 Published:2021-04-09
  • About author:WANG Le-le,born in 1985,Ph.D student.Her main research interests include information security and so on.(635718080@qq.com)
    WANG Bin-qiang,born in 1963,professor,Ph.D supervisor.His main research interests include network security and broad band information network.

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Abstract: Malware is a very threatening security problem in the Internet age.Due to the emergence of malicious programs and the speed up of propagation,it becomes more difficult to detect malicious programs.Most firewalls and antivirus software use a special set of bytes to identify malicious code based on malicious characteristics.However,a programmer of malicious program uses code obfuscation techniques to avoid this detection.Therefore,researchers use dynamic analysis method to combat this new malicious program,but the time efficiency and matching accuracy of this method are not satisfactory.This paper proposes an effective malicious behavior graph construction and matching algorithm,including the storage method of two-dimensional association graph,the construction method of behavior graph,the construction method of behavior association rules,the design of behavior graph parser,and the behavior matching algorithm.Finally,experimental verification analysis proves that this method has a high detection accuracy rate,except for the AutoRun category,the recognition rates for other types of malware are all above 90%.

Key words: Behavior correlation, Behavior graph, Behavior matching, Minimum behavior

CLC Number: 

  • TP393
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