2010 AutomaticMalwareCategorizationU

• (Ye et al., 2010) ⇒ Yanfang Ye, Tao Li, Yong Chen, and Qingshan Jiang. (2010). “Automatic Malware Categorization Using Cluster Ensemble.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835820

Subject Headings: Malware.

Cited By

• http://scholar.google.com/scholar?q=%22Automatic+malware+categorization+using+cluster+ensemble%22+2010
• http://portal.acm.org/citation.cfm?id=1835820&preflayout=flat#citedby

Quotes

Abstract

In this paper, resting on the analysis of instruction frequency and function-based instruction sequences, we develop an Automatic Malware Categorization System (AMCS) for automatically grouping malware samples into families that share some common characteristics using a cluster ensemble by aggregating the clustering solutions generated by different base clustering algorithms. We propose a principled cluster ensemble framework for combining individual clustering solutions based on the consensus partition. The domain knowledge in the form of sample-level constraints can be naturally incorporated in the ensemble framework. In addition, to account for the characteristics of feature representations, we propose a hybrid hierarchical clustering algorithm which combines the merits of hierarchical clustering and k-medoids algorithms and a weighted subspace K-medoids algorithm to generate base clusterings. The categorization results of our AMCS system can be used to generate signatures for malware families that are useful for malware detection. The case studies on large and real daily malware collection from Kingsoft Anti-Virus Lab demonstrate the effectiveness and efficiency of our AMCS system.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 AutomaticMalwareCategorizationU	Yanfang Ye Tao Li Yong Chen Qingshan Jiang			Automatic Malware Categorization Using Cluster Ensemble		KDD-2010 Proceedings		10.1145/1835804.1835820		2010