Name
Abstract | ||
|---|---|---|
Abstract In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs) on both static and dynamic feature sets and compare the resulting detection rates over a substantial number of malware families. We also consider hybrid cases, where dynamic analysis is used in the training phase, with static techniques used in the detection phase, and vice versa. In our experiments, a fully dynamic approach generally yields the best detection rates. We discuss the implications of this research for malware detection based on hybrid techniques. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11416-015-0261-z | J. Computer Virology and Hacking Techniques |
Field | DocType | Volume |
Data mining,Receiver operating characteristic,Control flow graph,Computer science,Precision and recall,Artificial intelligence,Hidden Markov model,Malware,Versa,Machine learning | Journal | 13 |
Issue | ISSN | Citations |
1 | 2263-8733 | 27 |
PageRank | References | Authors |
1.09 | 31 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anusha Damodaran | 1 | 27 | 1.09 |
| 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 |