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Feature selection and machine learning classification for malware detection

Malware is a computer security problem that can morph to evade traditional detection methods based on known signature matching. Since new malware variants contain patterns that are similar to those in observed malware, machine learning techniques can be used to identify new malware. This work presen...

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Main Authors: Khammas, Ban Mohammed, Monemi, Alireza, Bassi, Joseph Stephen, Ismail, Ismahani, Mohd. Nor, Sulaiman, Marsono, Muhammad Nadzir
Format: Article
Language:English
Published: Penerbit UTM Press 2015
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/55279/1/IsmahaniIsmail2015_FeatureSelectionandMachineLearningClassification.pdf
http://eprints.utm.my/id/eprint/55279/
http://dx.doi.org/10.11113/jt.v77.3558
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id my.utm.55279
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spelling my.utm.552792017-11-01T04:16:43Z http://eprints.utm.my/id/eprint/55279/ Feature selection and machine learning classification for malware detection Khammas, Ban Mohammed Monemi, Alireza Bassi, Joseph Stephen Ismail, Ismahani Mohd. Nor, Sulaiman Marsono, Muhammad Nadzir TK Electrical engineering. Electronics Nuclear engineering Malware is a computer security problem that can morph to evade traditional detection methods based on known signature matching. Since new malware variants contain patterns that are similar to those in observed malware, machine learning techniques can be used to identify new malware. This work presents a comparative study of several feature selection methods with four different machine learning classifiers in the context of static malware detection based on n-grams analysis. The result shows that the use of Principal Component Analysis (PCA) feature selection and Support Vector Machines (SVM) classification gives the best classification accuracy using a minimum number of features Penerbit UTM Press 2015-10 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/55279/1/IsmahaniIsmail2015_FeatureSelectionandMachineLearningClassification.pdf Khammas, Ban Mohammed and Monemi, Alireza and Bassi, Joseph Stephen and Ismail, Ismahani and Mohd. Nor, Sulaiman and Marsono, Muhammad Nadzir (2015) Feature selection and machine learning classification for malware detection. Jurnal Teknologi, 77 (1). pp. 243-250. ISSN 2180-3722 http://dx.doi.org/10.11113/jt.v77.3558 DOI:10.11113/jt.v77.3558
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Khammas, Ban Mohammed
Monemi, Alireza
Bassi, Joseph Stephen
Ismail, Ismahani
Mohd. Nor, Sulaiman
Marsono, Muhammad Nadzir
Feature selection and machine learning classification for malware detection
description Malware is a computer security problem that can morph to evade traditional detection methods based on known signature matching. Since new malware variants contain patterns that are similar to those in observed malware, machine learning techniques can be used to identify new malware. This work presents a comparative study of several feature selection methods with four different machine learning classifiers in the context of static malware detection based on n-grams analysis. The result shows that the use of Principal Component Analysis (PCA) feature selection and Support Vector Machines (SVM) classification gives the best classification accuracy using a minimum number of features
format Article
author Khammas, Ban Mohammed
Monemi, Alireza
Bassi, Joseph Stephen
Ismail, Ismahani
Mohd. Nor, Sulaiman
Marsono, Muhammad Nadzir
author_facet Khammas, Ban Mohammed
Monemi, Alireza
Bassi, Joseph Stephen
Ismail, Ismahani
Mohd. Nor, Sulaiman
Marsono, Muhammad Nadzir
author_sort Khammas, Ban Mohammed
title Feature selection and machine learning classification for malware detection
title_short Feature selection and machine learning classification for malware detection
title_full Feature selection and machine learning classification for malware detection
title_fullStr Feature selection and machine learning classification for malware detection
title_full_unstemmed Feature selection and machine learning classification for malware detection
title_sort feature selection and machine learning classification for malware detection
publisher Penerbit UTM Press
publishDate 2015
url http://eprints.utm.my/id/eprint/55279/1/IsmahaniIsmail2015_FeatureSelectionandMachineLearningClassification.pdf
http://eprints.utm.my/id/eprint/55279/
http://dx.doi.org/10.11113/jt.v77.3558
_version_ 1643653749575516160
score 13.211869

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