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Android malware detection technique via feature analysis

The rapidly increasing popularity of the Android platform has resulted in a significant increase in the number of malware compared to previous years. Since Android offers an open market model, it is an ideal target to launch malware attacks. Due to this problem, a lot of research work has been propo...

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Main Authors:	Ng, Ai Ping, Chiew, Kang Leng, Dayang Hanani, Binti Abang Ibrahim, Tiong, Wei King, Sze, San Nah, Nadianatra, binti Musa
Format:	Article
Language:	English
Published:	Taylor’s University 2018
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://ir.unimas.my/id/eprint/21484/1/ANDROID%20MALWARE%20DETECTION%20%20TECHNIQUE%20VIA%20FEATURE%20ANALYSIS%20%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/21484/ http://jestec.taylors.edu.my/
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id	my.unimas.ir.21484
record_format	eprints
spelling	my.unimas.ir.214842022-09-29T02:52:03Z http://ir.unimas.my/id/eprint/21484/ Android malware detection technique via feature analysis Ng, Ai Ping Chiew, Kang Leng Dayang Hanani, Binti Abang Ibrahim Tiong, Wei King Sze, San Nah Nadianatra, binti Musa T Technology (General) TA Engineering (General). Civil engineering (General) The rapidly increasing popularity of the Android platform has resulted in a significant increase in the number of malware compared to previous years. Since Android offers an open market model, it is an ideal target to launch malware attacks. Due to this problem, a lot of research work has been proposed to protect users from attacks. However, such protection cannot last long as attackers will usually find ways to defeat protection mechanism. As a result, this paper aims to develop an effective malware detection technique. The proposed method focuses on static analysis approach, which utilizes features from permissions, intents and API calls of an Android application. In order to create a sensitive and representative feature set, the proposed method also uses the correlation-based feature selection method. The final feature set will be fed into the support vector machine to perform the classification. Experimental results have shown that the proposed method achieved reliable detection accuracy at 95% and outperformed the benchmark method Taylor’s University 2018 Article PeerReviewed text en http://ir.unimas.my/id/eprint/21484/1/ANDROID%20MALWARE%20DETECTION%20%20TECHNIQUE%20VIA%20FEATURE%20ANALYSIS%20%20%28abstract%29.pdf Ng, Ai Ping and Chiew, Kang Leng and Dayang Hanani, Binti Abang Ibrahim and Tiong, Wei King and Sze, San Nah and Nadianatra, binti Musa (2018) Android malware detection technique via feature analysis. Journal of Engineering Science and Technology, July. pp. 78-90. ISSN 1823-4690 http://jestec.taylors.edu.my/
institution	Universiti Malaysia Sarawak
building	Centre for Academic Information Services (CAIS)
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sarawak
content_source	UNIMAS Institutional Repository
url_provider	http://ir.unimas.my/
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) Ng, Ai Ping Chiew, Kang Leng Dayang Hanani, Binti Abang Ibrahim Tiong, Wei King Sze, San Nah Nadianatra, binti Musa Android malware detection technique via feature analysis
description	The rapidly increasing popularity of the Android platform has resulted in a significant increase in the number of malware compared to previous years. Since Android offers an open market model, it is an ideal target to launch malware attacks. Due to this problem, a lot of research work has been proposed to protect users from attacks. However, such protection cannot last long as attackers will usually find ways to defeat protection mechanism. As a result, this paper aims to develop an effective malware detection technique. The proposed method focuses on static analysis approach, which utilizes features from permissions, intents and API calls of an Android application. In order to create a sensitive and representative feature set, the proposed method also uses the correlation-based feature selection method. The final feature set will be fed into the support vector machine to perform the classification. Experimental results have shown that the proposed method achieved reliable detection accuracy at 95% and outperformed the benchmark method
format	Article
author	Ng, Ai Ping Chiew, Kang Leng Dayang Hanani, Binti Abang Ibrahim Tiong, Wei King Sze, San Nah Nadianatra, binti Musa
author_facet	Ng, Ai Ping Chiew, Kang Leng Dayang Hanani, Binti Abang Ibrahim Tiong, Wei King Sze, San Nah Nadianatra, binti Musa
author_sort	Ng, Ai Ping
title	Android malware detection technique via feature analysis
title_short	Android malware detection technique via feature analysis
title_full	Android malware detection technique via feature analysis
title_fullStr	Android malware detection technique via feature analysis
title_full_unstemmed	Android malware detection technique via feature analysis
title_sort	android malware detection technique via feature analysis
publisher	Taylor’s University
publishDate	2018
url	http://ir.unimas.my/id/eprint/21484/1/ANDROID%20MALWARE%20DETECTION%20%20TECHNIQUE%20VIA%20FEATURE%20ANALYSIS%20%20%28abstract%29.pdf http://ir.unimas.my/id/eprint/21484/ http://jestec.taylors.edu.my/
_version_	1745566039355162624
score	13.211869

Android malware detection technique via feature analysis

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