Adjusting SVMs for Large Data Sets using Balanced Decision Trees
| Autor: | Cristina Vatamanu, Dragos Teodor Gavrilut, George Popoiu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
021110 strategic defence & security studies business.industry Computer science 0211 other engineering and technologies Decision tree 02 engineering and technology Machine learning computer.software_genre Support vector machine Footprint 03 medical and health sciences Identification (information) 030104 developmental biology False positive paradox Key (cryptography) Malware False positive rate Artificial intelligence business computer |
| Zdroj: | SYNASC |
| DOI: | 10.1109/synasc.2018.00043 |
| Popis: | While machine learning techniques were successfully used for malware identification, they were not without challenges. Over the years, several key points related to the usage of such algorithm for practical applications have evolved: low (close to 0) number of false positives, fast evaluation method, reasonable memory and disk footprint. Because of these constraints, security vendors had to chose a simple algorithm (that can meet all of the above requirements) instead of a more complex ones, even if the later had better detection rates. The present paper describes a hybrid approach that can be used in conjunction with an SVM classifier allowing us to overcome some of the above mentioned constraints. |
| Databáze: | OpenAIRE |
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