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Malware Clustering Based on SNN Density Using System Calls

  • Wang Shuwei
  • Wang Baosheng
  • Yong Tang
  • Yu Bo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9483)

Abstract

Clustering is an important part of the malware analysis. The malware clustering algorithms commonly used at present have gradually can not adapt to the growing number of malware. In order to improve the malware clustering algorithm, this paper uses the clustering algorithm based on Shared Nearest Neighbor (SNN), and uses frequencies of the system calls as the features for input. This algorithm combined with the DBSCAN which is traditional density-based clustering algorithm in data mining. This makes it is a better application in the process of clustering of malware. The results of clusters demonstrate that the effect of the algorithm of clustering is good. And the algorithm is simple to implement and easy to complete automated analysis. It can be applied to actual automated analysis of malware.

Keywords

Malware Clustering SNN System calls 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.National University of Defense TechnologyChangshaChina
  2. 2.Institute of Network and Information SecurityNational University of Defense TechnologyChangshaChina

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