SVM Training Phase Reduction Using Dataset Feature Filtering for Malware Detection

Philip O’Kane

doi:10.1109/TIFS.2013.2242890

SVM Training Phase Reduction Using Dataset Feature Filtering for Malware Detection

2013-03-01

50

OA

AI

DOI：10.1109/TIFS.2013.2242890

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Abstract

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N-gram analysis is an approach that investigates the structure of a program using bytes, characters, or text strings. A key issue with N-gram analysis is feature selection amidst the explosion of features that occurs when N is increased. The experiments within this paper represent programs as operational code (opcode) density histograms gained through dynamic analysis. A support vector machine is used to create a reference model, which is used to evaluate two methods of feature reduction, which are area of intersect and subspace analysis using eigenvectors. The findings show that the relationships between features are complex and simple statistics filtering approaches do not provide a viable approach. However, eigenvector subspace analysis produces a suitable filter.

Keywords:

KNN

metamorphism malware

obfuscation

packers

polymorphism

SVM

SVM Training Phase Reduction Using Dataset Feature Filtering for Malware Detection

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