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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Penerbit UTM Press
2015
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| オンライン･アクセス: | 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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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 |
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Universiti Teknologi Malaysia |
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UTM Library |
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Malaysia |
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Universiti Teknologi Malaysia |
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| language |
English |
| topic |
TK Electrical engineering. Electronics Nuclear engineering |
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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 |
13.251813 |