Name
Abstract | ||
|---|---|---|
With the increase in malware samples in the last decade more antivirus products started to use machine learning algorithms in order to cope with the large volume of data. Thanks to the good results and advances in learning infrastructure the neural networks have become one of the preferred way of addressing this. However, these algorithms need to be fine tuned in order to not add an overhead of costly false positives. This paper presents a study that takes a closer look into two techniques used for false positive mitigation issue: one side training and weight class adjustment. The techniques are used to train a neural network with zero false positives and are compared in order to find out which one give the highest true positive rate. Using a large dataset constructed over several years we show that by using these techniques a 90% true positive rate can be obtained while training for 0 false positives. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SYNASC49474.2019.00039 | 2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) |
Keywords | DocType | ISSN |
machine learning,artificial neural networks,false positives | Conference | 2470-8801 |
ISBN | Citations | PageRank |
978-1-7281-5725-2 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan-Georgian Marculet | 1 | 0 | 0.34 |
| Razvan Benchea | 2 | 26 | 3.76 |
| Dragos Teodor Gavrilut | 3 | 0 | 0.68 |