Bio-inspired for Features Optimization and Malware Detection

The leaking of sensitive data on Android mobile device poses a serious threat to users, and the unscrupulous attack violates the privacy of users. Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissi...

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Main Authors:	Mohd Faizal, Ab Razak, Nor Badrul, Anuar, Fazidah, Othman, Ahmad, Firdaus, Firdaus, Afifi, Rosli, Salleh
Format:	Article
Language:	English
Published:	Springer 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/23633/ http://umpir.ump.edu.my/id/eprint/23633/ http://umpir.ump.edu.my/id/eprint/23633/ http://umpir.ump.edu.my/id/eprint/23633/1/Bio-inspired%20for%20Features%20Optimization%20and%20Malware%20Detection1.pdf

id	ump-23633
recordtype	eprints
spelling	ump-236332019-01-07T06:36:32Z http://umpir.ump.edu.my/id/eprint/23633/ Bio-inspired for Features Optimization and Malware Detection Mohd Faizal, Ab Razak Nor Badrul, Anuar Fazidah, Othman Ahmad, Firdaus Firdaus, Afifi Rosli, Salleh QA75 Electronic computers. Computer science The leaking of sensitive data on Android mobile device poses a serious threat to users, and the unscrupulous attack violates the privacy of users. Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. This paper aims to evaluate the effectiveness of the machine learning approach for detecting Android malware. In this paper, we applied the bio-inspired algorithm as a feature optimization approach for selecting reliable permission features that able to identify malware attacks. A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. This technique shows that the use of Android permissions is a potential feature for malware detection. The study compares the bio-inspired algorithm [particle swarm optimization (PSO)] and the evolutionary computation with information gain to find the best features optimization in selecting features. The features were optimized from 378 to 11 by using bio-inspired algorithm: particle swarm optimization (PSO). The evaluation utilizes 5000 Drebin malware samples and 3500 benign samples. In recognizing the Android malware, it appears that AdaBoost is able to achieve good detection accuracy with a true positive rate value of 95.6%, using Android permissions. The results show that particle swarm optimization (PSO) is the best feature optimization approach for selecting features. Springer 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23633/1/Bio-inspired%20for%20Features%20Optimization%20and%20Malware%20Detection1.pdf Mohd Faizal, Ab Razak and Nor Badrul, Anuar and Fazidah, Othman and Ahmad, Firdaus and Firdaus, Afifi and Rosli, Salleh (2018) Bio-inspired for Features Optimization and Malware Detection. Arabian Journal for Science and Engineering, 43 (12). pp. 6963-6979. ISSN 1319-8025 (print); 2191-4281 (online) https://doi.org/10.1007/s13369-017-2951-y https://doi.org/10.1007/s13369-017-2951-y
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Mohd Faizal, Ab Razak Nor Badrul, Anuar Fazidah, Othman Ahmad, Firdaus Firdaus, Afifi Rosli, Salleh Bio-inspired for Features Optimization and Malware Detection
description	The leaking of sensitive data on Android mobile device poses a serious threat to users, and the unscrupulous attack violates the privacy of users. Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. This paper aims to evaluate the effectiveness of the machine learning approach for detecting Android malware. In this paper, we applied the bio-inspired algorithm as a feature optimization approach for selecting reliable permission features that able to identify malware attacks. A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. This technique shows that the use of Android permissions is a potential feature for malware detection. The study compares the bio-inspired algorithm [particle swarm optimization (PSO)] and the evolutionary computation with information gain to find the best features optimization in selecting features. The features were optimized from 378 to 11 by using bio-inspired algorithm: particle swarm optimization (PSO). The evaluation utilizes 5000 Drebin malware samples and 3500 benign samples. In recognizing the Android malware, it appears that AdaBoost is able to achieve good detection accuracy with a true positive rate value of 95.6%, using Android permissions. The results show that particle swarm optimization (PSO) is the best feature optimization approach for selecting features.
format	Article
author	Mohd Faizal, Ab Razak Nor Badrul, Anuar Fazidah, Othman Ahmad, Firdaus Firdaus, Afifi Rosli, Salleh
author_facet	Mohd Faizal, Ab Razak Nor Badrul, Anuar Fazidah, Othman Ahmad, Firdaus Firdaus, Afifi Rosli, Salleh
author_sort	Mohd Faizal, Ab Razak
title	Bio-inspired for Features Optimization and Malware Detection
title_short	Bio-inspired for Features Optimization and Malware Detection
title_full	Bio-inspired for Features Optimization and Malware Detection
title_fullStr	Bio-inspired for Features Optimization and Malware Detection
title_full_unstemmed	Bio-inspired for Features Optimization and Malware Detection
title_sort	bio-inspired for features optimization and malware detection
publisher	Springer
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23633/ http://umpir.ump.edu.my/id/eprint/23633/ http://umpir.ump.edu.my/id/eprint/23633/ http://umpir.ump.edu.my/id/eprint/23633/1/Bio-inspired%20for%20Features%20Optimization%20and%20Malware%20Detection1.pdf
first_indexed	2023-09-18T22:35:29Z
last_indexed	2023-09-18T22:35:29Z
_version_	1777416566917300224

Bio-inspired for Features Optimization and Malware Detection

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