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ACM Computing Surveys (CSUR)
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
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IMDS: intelligent malware detection system
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ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Generalized cluster aggregation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Automated classification and analysis of internet malware
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Survey of clustering algorithms
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AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Malware characterization using behavioral components
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A comparative study of malware family classification
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Review: Classification of malware based on integrated static and dynamic features
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DUET: integration of dynamic and static analyses for malware clustering with cluster ensembles
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Towards automatic software lineage inference
SEC'13 Proceedings of the 22nd USENIX conference on Security
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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.