Open access Journal SINTA 1
Recommendation mobile antivirus for Android smartphones based on malware detection
IAES International Journal of Artificial Intelligence · Vol. 13 No. 3 (2024) · pp. 3559–3566
- ISSN
- 2252-8938, 2089-4872
- Published
- 2024-09-01
- Publisher
- Institute of Advanced Engineering and Science
- Language
- eng
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-author | Organisation | Joint articles |
|---|---|---|
| Cahli Suhendi | Affiliation unknown | 2 |
| Lea Kristi Agustina | Affiliation unknown | 2 |
| Maimunah Maskur | Binus University | 1 |
| Hendra Kurniawan | Binus University | 1 |
| Deni Anggraini | Universitas Halu Oleo | 1 |
| Anita Lukman | Affiliation unknown | 1 |
| N Nurfieftien | Affiliation unknown | 1 |
| Suma'inna Suma'inna | Affiliation unknown | 1 |
| Muhammad Manaqib | Syarif Hidayatullah State Islamic University Jakarta | 1 |
| Nurul Burhan | Affiliation unknown | 1 |
| rangga dino Alfian | Affiliation unknown | 1 |
| Fahmy Zuhda Bahtiar | Affiliation unknown | 1 |
| Erlangga Ibrahim | Affiliation unknown | 1 |
| Hikhmadhan Gultaf | Affiliation unknown | 1 |
| Virgian Rahmanda | Affiliation unknown | 1 |
| M. R. P. Sudibyo | Affiliation unknown | 1 |
| Reza Rizki | Affiliation unknown | 1 |
| Luhut P. Siringorongo | Affiliation unknown | 1 |
| La Ode Arham | Affiliation unknown | 1 |
| Fika Rofiek Mufakhir | Affiliation unknown | 1 |
| Mohamad Annas | Affiliation unknown | 1 |
| Rahayu Pristiwati | Affiliation unknown | 1 |
| Hari Bakti Mardikantoro | Affiliation unknown | 1 |
| Rustono Rustono | Affiliation unknown | 1 |
| Muhammad Safri | Affiliation unknown | 1 |
| Rosyani Rosyani | Affiliation unknown | 1 |
| Bhakti Prabandyo Wicaksono | Affiliation unknown | 1 |
| Satrio Sadewo | Affiliation unknown | 1 |
| Rinaldi Rinaldi | Affiliation unknown | 1 |
| Khusnul Setia Wardani | National Research and Innovation Agency | 1 |
| Sigit Sutikno | Affiliation unknown | 1 |
| Muhamad Yusa | Affiliation unknown | 1 |
| Ahmad Muhammad | Affiliation unknown | 1 |
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.