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Recommendation mobile antivirus for Android smartphones based on malware detection

IAES International Journal of Artificial Intelligence · Vol. 13 No. 3 (2024) · hlm. 3559–3566

DOI
10.11591/ijai.v13.i3.pp3559-3566
ISSN
2252-8938, 2089-4872
Terbit
2024-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The proliferation of smartphone malware attacks due to a lack of vigilance in app selection raises serious concerns. Built-in smartphone security features often must be improved to protect devices from these threats. Although numerous articles recommend top-tier antivirus solutions, there need to be more reliable data sources that raise suspicions about undisclosed promotional motives. This research endeavors to establish a ranking of antivirus efficacy to provide optimal recommendations for Android smartphone users. The research methodology entails a meticulous comparison of malware detection and labeling outcomes between various antivirus programs within Virustotal and the labeling system employed by the Euphony application. The comparative results are categorized into three groups: antivirus solutions proficient in identifying specific malware types, those detecting malware presence without categorization, and antivirus software failing to detect malware effectively. The experimental findings present the five leading antivirus solutions, ranked from the highest to lowest scores, as Ikarus, Fortinet, ESET-NOD32, Avast-Mobile, and SymantecMobileInsight. Based on the comprehensive assessment conducted in this study, these solutions are recommended as the top antivirus choices. These recommendations are poised to significantly aid users in selecting the most suitable antivirus protection for their Android smartphones.

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  • Binus University
  • Universitas Halu Oleo
  • Syarif Hidayatullah State Islamic University Jakarta
  • National Research and Innovation Agency
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Cahli SuhendiAfiliasi tidak diketahui2
Lea Kristi AgustinaAfiliasi tidak diketahui2
Maimunah MaskurBinus University1
Hendra KurniawanBinus University1
Deni AnggrainiUniversitas Halu Oleo1
Anita LukmanAfiliasi tidak diketahui1
N NurfieftienAfiliasi tidak diketahui1
Suma'inna Suma'innaAfiliasi tidak diketahui1
Muhammad ManaqibSyarif Hidayatullah State Islamic University Jakarta1
Nurul BurhanAfiliasi tidak diketahui1
rangga dino AlfianAfiliasi tidak diketahui1
Fahmy Zuhda BahtiarAfiliasi tidak diketahui1
Erlangga IbrahimAfiliasi tidak diketahui1
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Virgian RahmandaAfiliasi tidak diketahui1
M. R. P. SudibyoAfiliasi tidak diketahui1
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Luhut P. SiringorongoAfiliasi tidak diketahui1
La Ode ArhamAfiliasi tidak diketahui1
Fika Rofiek MufakhirAfiliasi tidak diketahui1
Mohamad AnnasAfiliasi tidak diketahui1
Rahayu PristiwatiAfiliasi tidak diketahui1
Hari Bakti MardikantoroAfiliasi tidak diketahui1
Rustono RustonoAfiliasi tidak diketahui1
Muhammad SafriAfiliasi tidak diketahui1
Rosyani RosyaniAfiliasi tidak diketahui1
Bhakti Prabandyo WicaksonoAfiliasi tidak diketahui1
Satrio SadewoAfiliasi tidak diketahui1
Rinaldi RinaldiAfiliasi tidak diketahui1
Khusnul Setia WardaniNational Research and Innovation Agency1
Sigit SutiknoAfiliasi tidak diketahui1
Muhamad YusaAfiliasi tidak diketahui1
Ahmad MuhammadAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Recommendation mobile antivirus for Android smartphones based on malware detection</dc:title>
	<dc:creator>Saputra, Hendra</dc:creator>
	<dc:creator>Zahra, Amalia</dc:creator>
	<dc:creator>Faldi, Faldi</dc:creator>
	<dc:creator>Fadzlul Rahman, Ferry</dc:creator>
	<dc:creator>Harits, Sayekti</dc:creator>
	<dc:creator>Joko Pranoto, Wawan</dc:creator>
	<dc:creator>Rahman, Fathur</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Android malware; Best antivirus; Euphony; Virusshare; Virustotal</dc:subject>
	<dc:description xml:lang="en-US">The proliferation of smartphone malware attacks due to a lack of vigilance in app selection raises serious concerns. Built-in smartphone security features often must be improved to protect devices from these threats. Although numerous articles recommend top-tier antivirus solutions, there need to be more reliable data sources that raise suspicions about undisclosed promotional motives. This research endeavors to establish a ranking of antivirus efficacy to provide optimal recommendations for Android smartphone users. The research methodology entails a meticulous comparison of malware detection and labeling outcomes between various antivirus programs within Virustotal and the labeling system employed by the Euphony application. The comparative results are categorized into three groups: antivirus solutions proficient in identifying specific malware types, those detecting malware presence without categorization, and antivirus software failing to detect malware effectively. The experimental findings present the five leading antivirus solutions, ranked from the highest to lowest scores, as Ikarus, Fortinet, ESET-NOD32, Avast-Mobile, and SymantecMobileInsight. Based on the comprehensive assessment conducted in this study, these solutions are recommended as the top antivirus choices. These recommendations are poised to significantly aid users in selecting the most suitable antivirus protection for their Android smartphones.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24095</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i3.pp3559-3566</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 3559-3566</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24095/14167</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>