Name
Abstract | ||
|---|---|---|
We propose a versatile framework in which one can employ different machine learning algorithms to successfully distinguish between malware files and clean files, while aimi ng to minimise the number of false positives. In this paper we present the ideas behind our framework by working firstly with cascade one-sided perceptrons and secondly with cascade kernelized one-sided perceptrons. After having been successfully tested on medium-size datasets of malware and clean files, th e ideas behind this framework were submitted to a scaling-up process that enable us to work with very large datasets of malware and clean files. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IMCSIT.2009.5352759 | IMCSIT |
Keywords | Field | DocType |
machine learning,learning artificial intelligence,perceptrons | Data mining,Computer science,Artificial intelligence,Malware,Perceptron,Machine learning,False positive paradox | Conference |
Citations | PageRank | References |
14 | 1.12 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dragos Gavrilut | 1 | 62 | 7.95 |
| Mihai Cimpoesu | 2 | 21 | 2.78 |
| Dan Anton | 3 | 22 | 6.94 |
| Liviu Ciortuz | 4 | 24 | 4.84 |