In this research, we apply clustering techniques to the malware classification problem. We compute clusters using the well-known K-means and Expectation Maximization algorithms, with the underlying scores based on Hidden Markov Models. We compare the results obtained from these two clustering approaches and we carefully consider the interplay between the dimension (i.e., number of models used for clustering), and the number of clusters, with respect to the accuracy of the clustering.
['Swathi Pai', 'Fabio Di Troia', 'Corrado Aaron Visaggio', 'Thomas H. Austin', 'Mark Stamp']
Clustering for malware classification