A score based malware classification approach for mobile forensic analysis

The rapid growth of Android as one of the leading Operating System (OS) for mobile devices drives the need of effective security measures to ensure the users have a safer platform to use. Boolean based features used for application permissions degrades the precision, recall, F-1 score and accuracy o...

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Main Author:	Gobi, Ramyaa
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Mobile device forensics Computer crimes - Investigation
Online Access:	http://psasir.upm.edu.my/id/eprint/83857/1/FSKTM%202019%2040%20-IR.pdf
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id	my-upm-ir.83857
record_format	uketd_dc
spelling	my-upm-ir.838572020-10-23T09:35:31Z A score based malware classification approach for mobile forensic analysis 2019-06 Gobi, Ramyaa The rapid growth of Android as one of the leading Operating System (OS) for mobile devices drives the need of effective security measures to ensure the users have a safer platform to use. Boolean based features used for application permissions degrades the precision, recall, F-1 score and accuracy of malware detection. The reason for this is that Boolean based features classify the benign and malware applications based on true or false rule which is done based on the binary 0 for benign and 1 for malware. FAMOUS (Forensic Analysis of MObile devices Using Scoring of application permissions) which incorporates Effective Maliciousness Score of Permission (EMSP), a score based representation for permissions which replaces the Boolean representation for permissions have produced better result for the accuracy, precision, recall and F1-score over the Boolean based feature from existing works. FAMOUS is tested on the crawled datasets that are collected from multiple public archives such as Cantagio dump, AndroMalShare, Derbin project and Andrototal. This crawled datasets are then labelled by the result captured from Virus Total engines. Thus, FAMOUS did not use any standard dataset for its analysis. In his research, we will implement the EMSP, a score based triage and test it over Android Malware Dataset (AMD) and Android PRAGuard dataset to ensure reliable result obtained for the Accuracy, Precision, Recall and F1-Score through Machine Learning classifiers. Total of five classifiers have been used to train and test the datasets which consist of Random Forest, Decision Tree, Naive Bayes, K-nearest neighbours, and Support Vector Machine. EMSP will be implemented using Python programming language on Windows system. The performance metrics evaluated for the research are precision, recall, F-1 score and accuracy. The accuracy obtained varies for different classifiers for AMD and Android PRAGuard dataset. The best result obtained for Random Forest classifier when using AMD. Mobile device forensics Computer crimes - Investigation 2019-06 Thesis http://psasir.upm.edu.my/id/eprint/83857/ http://psasir.upm.edu.my/id/eprint/83857/1/FSKTM%202019%2040%20-IR.pdf text en public masters Universiti Putra Malaysia Mobile device forensics Computer crimes - Investigation Mohd Hanapi, Zurina
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Mohd Hanapi, Zurina
topic	Mobile device forensics Computer crimes - Investigation
spellingShingle	Mobile device forensics Computer crimes - Investigation Gobi, Ramyaa A score based malware classification approach for mobile forensic analysis
description	The rapid growth of Android as one of the leading Operating System (OS) for mobile devices drives the need of effective security measures to ensure the users have a safer platform to use. Boolean based features used for application permissions degrades the precision, recall, F-1 score and accuracy of malware detection. The reason for this is that Boolean based features classify the benign and malware applications based on true or false rule which is done based on the binary 0 for benign and 1 for malware. FAMOUS (Forensic Analysis of MObile devices Using Scoring of application permissions) which incorporates Effective Maliciousness Score of Permission (EMSP), a score based representation for permissions which replaces the Boolean representation for permissions have produced better result for the accuracy, precision, recall and F1-score over the Boolean based feature from existing works. FAMOUS is tested on the crawled datasets that are collected from multiple public archives such as Cantagio dump, AndroMalShare, Derbin project and Andrototal. This crawled datasets are then labelled by the result captured from Virus Total engines. Thus, FAMOUS did not use any standard dataset for its analysis. In his research, we will implement the EMSP, a score based triage and test it over Android Malware Dataset (AMD) and Android PRAGuard dataset to ensure reliable result obtained for the Accuracy, Precision, Recall and F1-Score through Machine Learning classifiers. Total of five classifiers have been used to train and test the datasets which consist of Random Forest, Decision Tree, Naive Bayes, K-nearest neighbours, and Support Vector Machine. EMSP will be implemented using Python programming language on Windows system. The performance metrics evaluated for the research are precision, recall, F-1 score and accuracy. The accuracy obtained varies for different classifiers for AMD and Android PRAGuard dataset. The best result obtained for Random Forest classifier when using AMD.
format	Thesis
qualification_level	Master's degree
author	Gobi, Ramyaa
author_facet	Gobi, Ramyaa
author_sort	Gobi, Ramyaa
title	A score based malware classification approach for mobile forensic analysis
title_short	A score based malware classification approach for mobile forensic analysis
title_full	A score based malware classification approach for mobile forensic analysis
title_fullStr	A score based malware classification approach for mobile forensic analysis
title_full_unstemmed	A score based malware classification approach for mobile forensic analysis
title_sort	score based malware classification approach for mobile forensic analysis
granting_institution	Universiti Putra Malaysia
publishDate	2019
url	http://psasir.upm.edu.my/id/eprint/83857/1/FSKTM%202019%2040%20-IR.pdf
_version_	1747813423460122624

A score based malware classification approach for mobile forensic analysis

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