Partitional Clustering of Malware Using K-Means

Renato Cordeiro De Amorim; Peter Komisarczuk

doi:10.1007/978-3-319-04447-7_18

Partitional Clustering of Malware Using K-Means

Renato Cordeiro De Amorim, Peter Komisarczuk

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

3 Citations (Scopus)

Abstract

This paper describes a novel method aiming to cluster datasets containing malware behavioural data. Our method transform the data into an standardised data matrix that can be used in any clustering algorithm, finds the number of clusters in the data set and includes an optional visualization step for high-dimensional data using principal component analysis. Our clustering method deals well with categorical data, and it is able to cluster the behavioural data of 17,000 websites, acquired with Capture-HPC, in less than 2 min

Original language	English
Title of host publication	Cyberpatterns
Subtitle of host publication	Unifying Design Patterns with Security and Attack Patterns
Publisher	Springer Nature Link
Pages	223-233
ISBN (Electronic)	978-3-319-04447-7
ISBN (Print)	978-3-319-04446-0
DOIs	https://doi.org/10.1007/978-3-319-04447-7_18
Publication status	Published - May 2014

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10.1007/978-3-319-04447-7_18

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Partitional Clustering of Malware Using K-Means

Abstract

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