Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11416-016-0265-3 | J. Computer Virology and Hacking Techniques |
Keywords | Field | DocType |
Receiver Operating Characteristic Curve, Hide Markov Model, Expectation Maximization, Hide Markov Model Model, Silhouette Coefficient | Data mining,Fuzzy clustering,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Cluster analysis,Brown clustering,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
13 | 2 | 2263-8733 |
Citations | PageRank | References |
4 | 0.42 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Swathi Pai | 1 | 4 | 0.42 |
| Fabio Di Troia | 2 | 41 | 3.12 |
| Corrado Aaron Visaggio | 3 | 619 | 45.84 |
| Thomas H. Austin | 4 | 307 | 15.96 |
| Mark Stamp | 5 | 513 | 33.32 |