  In this paper, we use $K$-means clustering to analyze various relationships
between malware samples. We consider a dataset comprising~20 malware families
with~1000 samples per family. These families can be categorized into seven
different types of malware. We perform clustering based on pairs of families
and use the results to determine relationships between families. We perform a
similar cluster analysis based on malware type. Our results indicate that
$K$-means clustering can be a powerful tool for data exploration of malware
family relationships.
