Name
Abstract | ||
|---|---|---|
Malware creators have been getting their way for too long now. String-based similarity measures can leverage ground truth in a scalable way and can operate at a level of abstraction that is difficult to combat from the code level. We introduce ITect, a scalable approach to malware similarity detection based on information theory. ITect targets file entropy patterns in different ways to achieve 100% precision with 90% accuracy but it could target 100% recall instead. It outperforms VirusTotal for precision and accuracy on combined Kaggle and VirusShare malware. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Cryptography and Security | Information theory,Abstraction,Computer science,Computer security,Ground truth,Malware,Scalability |
DocType | Volume | Citations |
Journal | abs/1609.02404 | 1 |
PageRank | References | Authors |
0.36 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sukriti Bhattacharya | 1 | 1 | 1.04 |
| Héctor Menéndez | 2 | 171 | 15.75 |
| Earl T. Barr | 3 | 468 | 15.46 |
| David M. Clark | 4 | 153 | 16.33 |