Discovering optimal features using static analysis and a genetic search based method for Android malware detection

Mobile device manufacturers are rapidly producing miscellaneous android versions worldwide. Simultaneously, cyber criminals are executing malicious actions such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as many people use a...

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Main Authors:	Ahmad Firdaus, Zainal Abidin, Nor Badrul, Anuar, Ahmad, Karim, Mohd Faizal, Ab Razak
Format:	Article
Language:	English English
Published:	Springer 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/1/Discovering%20optimal%20features%20using%20static.pdf http://umpir.ump.edu.my/id/eprint/19177/2/Discovering%20optimal%20features%20using%20static1.pdf

id	ump-19177
recordtype	eprints
spelling	ump-191772018-08-10T03:29:39Z http://umpir.ump.edu.my/id/eprint/19177/ Discovering optimal features using static analysis and a genetic search based method for Android malware detection Ahmad Firdaus, Zainal Abidin Nor Badrul, Anuar Ahmad, Karim Mohd Faizal, Ab Razak QA75 Electronic computers. Computer science Mobile device manufacturers are rapidly producing miscellaneous android versions worldwide. Simultaneously, cyber criminals are executing malicious actions such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as many people use android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. In this study, we used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimal number of features to classify malware efficiently. Therefore, we used genetic search (GS), which is a search based on a genetic algorithm (GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), Functional Trees (FT), J48, Random Forest (RF), and Multilayer Perceptron (MLP). Among these classifiers, FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use of only six features. Springer 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19177/1/Discovering%20optimal%20features%20using%20static.pdf pdf en http://umpir.ump.edu.my/id/eprint/19177/2/Discovering%20optimal%20features%20using%20static1.pdf Ahmad Firdaus, Zainal Abidin and Nor Badrul, Anuar and Ahmad, Karim and Mohd Faizal, Ab Razak (2018) Discovering optimal features using static analysis and a genetic search based method for Android malware detection. Frontiers of Information Technology & Electronic Engineering, 19 (6). pp. 712-736. ISSN 2095-9230 https://doi.org/10.1631/FITEE.1601491 doi: 10.1631/FITEE.1601491
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Ahmad Firdaus, Zainal Abidin Nor Badrul, Anuar Ahmad, Karim Mohd Faizal, Ab Razak Discovering optimal features using static analysis and a genetic search based method for Android malware detection
description	Mobile device manufacturers are rapidly producing miscellaneous android versions worldwide. Simultaneously, cyber criminals are executing malicious actions such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as many people use android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. In this study, we used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimal number of features to classify malware efficiently. Therefore, we used genetic search (GS), which is a search based on a genetic algorithm (GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), Functional Trees (FT), J48, Random Forest (RF), and Multilayer Perceptron (MLP). Among these classifiers, FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use of only six features.
format	Article
author	Ahmad Firdaus, Zainal Abidin Nor Badrul, Anuar Ahmad, Karim Mohd Faizal, Ab Razak
author_facet	Ahmad Firdaus, Zainal Abidin Nor Badrul, Anuar Ahmad, Karim Mohd Faizal, Ab Razak
author_sort	Ahmad Firdaus, Zainal Abidin
title	Discovering optimal features using static analysis and a genetic search based method for Android malware detection
title_short	Discovering optimal features using static analysis and a genetic search based method for Android malware detection
title_full	Discovering optimal features using static analysis and a genetic search based method for Android malware detection
title_fullStr	Discovering optimal features using static analysis and a genetic search based method for Android malware detection
title_full_unstemmed	Discovering optimal features using static analysis and a genetic search based method for Android malware detection
title_sort	discovering optimal features using static analysis and a genetic search based method for android malware detection
publisher	Springer
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/1/Discovering%20optimal%20features%20using%20static.pdf http://umpir.ump.edu.my/id/eprint/19177/2/Discovering%20optimal%20features%20using%20static1.pdf
first_indexed	2023-09-18T22:27:29Z
last_indexed	2023-09-18T22:27:29Z
_version_	1777416063320850432

Discovering optimal features using static analysis and a genetic search based method for Android malware detection

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