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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) · pp. 3559–3566

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

Abstract

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.

Keywords

Collaboration network of the authors

40 authors · 99 co-authorship links · 1 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Binus University
  • Universitas Halu Oleo
  • Syarif Hidayatullah State Islamic University Jakarta
  • National Research and Innovation Agency
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Cahli SuhendiAffiliation unknown2
Lea Kristi AgustinaAffiliation unknown2
Maimunah MaskurBinus University1
Hendra KurniawanBinus University1
Deni AnggrainiUniversitas Halu Oleo1
Anita LukmanAffiliation unknown1
N NurfieftienAffiliation unknown1
Suma'inna Suma'innaAffiliation unknown1
Muhammad ManaqibSyarif Hidayatullah State Islamic University Jakarta1
Nurul BurhanAffiliation unknown1
rangga dino AlfianAffiliation unknown1
Fahmy Zuhda BahtiarAffiliation unknown1
Erlangga IbrahimAffiliation unknown1
Hikhmadhan GultafAffiliation unknown1
Virgian RahmandaAffiliation unknown1
M. R. P. SudibyoAffiliation unknown1
Reza RizkiAffiliation unknown1
Luhut P. SiringorongoAffiliation unknown1
La Ode ArhamAffiliation unknown1
Fika Rofiek MufakhirAffiliation unknown1
Mohamad AnnasAffiliation unknown1
Rahayu PristiwatiAffiliation unknown1
Hari Bakti MardikantoroAffiliation unknown1
Rustono RustonoAffiliation unknown1
Muhammad SafriAffiliation unknown1
Rosyani RosyaniAffiliation unknown1
Bhakti Prabandyo WicaksonoAffiliation unknown1
Satrio SadewoAffiliation unknown1
Rinaldi RinaldiAffiliation unknown1
Khusnul Setia WardaniNational Research and Innovation Agency1
Sigit SutiknoAffiliation unknown1
Muhamad YusaAffiliation unknown1
Ahmad MuhammadAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc)
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	<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>