Supervised machine learning approach for detection of malicious executables

Malware can be described as any type of malicious code that has the potential harm to the computer or network. these threats came from various sources like the internet, local networks and portable drives. Virus which replicates itself is growing faster every year and poses a serious global security...

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Main Author:	Ahmed, Yahye Abukar
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/33296/5/YahyeAbukarAhmedMFSKSM2013.pdf
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id	my-utm-ep.33296
record_format	uketd_dc
spelling	my-utm-ep.332962017-07-25T08:22:13Z Supervised machine learning approach for detection of malicious executables 2013-01 Ahmed, Yahye Abukar QA75 Electronic computers. Computer science Malware can be described as any type of malicious code that has the potential harm to the computer or network. these threats came from various sources like the internet, local networks and portable drives. Virus which replicates itself is growing faster every year and poses a serious global security threat. The purpose of this research is to classify portable executable new malicious files from benign files. In recent years, data mining methods are investigated for detecting unknown malicious executables, and the result show high and acceptable detection rate. Therefore, this project applied machine learning to detect malicious executable files through Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. These algorithms are compared together and selected the best accuracy model. The result of this research indicated that the accuracy of the SVM and ANN rely on the settings of the parameters used; ANN showed higher accuracy of 98.76 than SVM in terms of data set used while SVM performed a speed three times less than ANN and low computational power. The main conclusions drawn from this research were that current detection approaches of the antivirus are deficient because they fail to detect new unseen malicious files and they have higher false negative rates. 2013-01 Thesis http://eprints.utm.my/id/eprint/33296/ http://eprints.utm.my/id/eprint/33296/5/YahyeAbukarAhmedMFSKSM2013.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ahmed, Yahye Abukar Supervised machine learning approach for detection of malicious executables
description	Malware can be described as any type of malicious code that has the potential harm to the computer or network. these threats came from various sources like the internet, local networks and portable drives. Virus which replicates itself is growing faster every year and poses a serious global security threat. The purpose of this research is to classify portable executable new malicious files from benign files. In recent years, data mining methods are investigated for detecting unknown malicious executables, and the result show high and acceptable detection rate. Therefore, this project applied machine learning to detect malicious executable files through Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms. These algorithms are compared together and selected the best accuracy model. The result of this research indicated that the accuracy of the SVM and ANN rely on the settings of the parameters used; ANN showed higher accuracy of 98.76 than SVM in terms of data set used while SVM performed a speed three times less than ANN and low computational power. The main conclusions drawn from this research were that current detection approaches of the antivirus are deficient because they fail to detect new unseen malicious files and they have higher false negative rates.
format	Thesis
qualification_level	Master's degree
author	Ahmed, Yahye Abukar
author_facet	Ahmed, Yahye Abukar
author_sort	Ahmed, Yahye Abukar
title	Supervised machine learning approach for detection of malicious executables
title_short	Supervised machine learning approach for detection of malicious executables
title_full	Supervised machine learning approach for detection of malicious executables
title_fullStr	Supervised machine learning approach for detection of malicious executables
title_full_unstemmed	Supervised machine learning approach for detection of malicious executables
title_sort	supervised machine learning approach for detection of malicious executables
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2013
url	http://eprints.utm.my/id/eprint/33296/5/YahyeAbukarAhmedMFSKSM2013.pdf
_version_	1747816127174541312

Supervised machine learning approach for detection of malicious executables

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