Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer
Smartphone usage is nearly ubiquitous worldwide, and Android provides the leading open-source operating system, retaining the most significant market share and active user population of all open-source operating systems. Hence, malicious actors target the Android operating system to capitalize on th...
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| Published in: | PloS one Vol. 16; no. 11; p. e0260232 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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United States
Public Library of Science
19.11.2021
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Smartphone usage is nearly ubiquitous worldwide, and Android provides the leading open-source operating system, retaining the most significant market share and active user population of all open-source operating systems. Hence, malicious actors target the Android operating system to capitalize on this consumer reliance and vulnerabilities present in the system. Hackers often use confidential user data to exploit users for advertising, extortion, and theft. Notably, most Android malware detection tools depend on conventional machine-learning algorithms; hence, they lose the benefits of metaheuristic optimization. Here, we introduce a novel detection system based on optimizing the random vector functional link (RVFL) using the artificial Jellyfish Search (JS) optimizer following dimensional reduction of Android application features. JS is used to determine the optimal configurations of RVFL to improve classification performance. RVFL+JS minimizes the runtime of the execution of the optimized models with the best performance metrics, based on a dataset consisting of 11,598 multi-class applications and 471 static and dynamic features. |
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| AbstractList | Smartphone usage is nearly ubiquitous worldwide, and Android provides the leading open-source operating system, retaining the most significant market share and active user population of all open-source operating systems. Hence, malicious actors target the Android operating system to capitalize on this consumer reliance and vulnerabilities present in the system. Hackers often use confidential user data to exploit users for advertising, extortion, and theft. Notably, most Android malware detection tools depend on conventional machine-learning algorithms; hence, they lose the benefits of metaheuristic optimization. Here, we introduce a novel detection system based on optimizing the random vector functional link (RVFL) using the artificial Jellyfish Search (JS) optimizer following dimensional reduction of Android application features. JS is used to determine the optimal configurations of RVFL to improve classification performance. RVFL+JS minimizes the runtime of the execution of the optimized models with the best performance metrics, based on a dataset consisting of 11,598 multi-class applications and 471 static and dynamic features. Smartphone usage is nearly ubiquitous worldwide, and Android provides the leading open-source operating system, retaining the most significant market share and active user population of all open-source operating systems. Hence, malicious actors target the Android operating system to capitalize on this consumer reliance and vulnerabilities present in the system. Hackers often use confidential user data to exploit users for advertising, extortion, and theft. Notably, most Android malware detection tools depend on conventional machine-learning algorithms; hence, they lose the benefits of metaheuristic optimization. Here, we introduce a novel detection system based on optimizing the random vector functional link (RVFL) using the artificial Jellyfish Search (JS) optimizer following dimensional reduction of Android application features. JS is used to determine the optimal configurations of RVFL to improve classification performance. RVFL+JS minimizes the runtime of the execution of the optimized models with the best performance metrics, based on a dataset consisting of 11,598 multi-class applications and 471 static and dynamic features.Smartphone usage is nearly ubiquitous worldwide, and Android provides the leading open-source operating system, retaining the most significant market share and active user population of all open-source operating systems. Hence, malicious actors target the Android operating system to capitalize on this consumer reliance and vulnerabilities present in the system. Hackers often use confidential user data to exploit users for advertising, extortion, and theft. Notably, most Android malware detection tools depend on conventional machine-learning algorithms; hence, they lose the benefits of metaheuristic optimization. Here, we introduce a novel detection system based on optimizing the random vector functional link (RVFL) using the artificial Jellyfish Search (JS) optimizer following dimensional reduction of Android application features. JS is used to determine the optimal configurations of RVFL to improve classification performance. RVFL+JS minimizes the runtime of the execution of the optimized models with the best performance metrics, based on a dataset consisting of 11,598 multi-class applications and 471 static and dynamic features. |
| Audience | Academic |
| Author | Mostafa, Reham R. Elkabbash, Emad T. Barakat, Sherif I. |
| AuthorAffiliation | Torrens University Australia, AUSTRALIA Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt |
| AuthorAffiliation_xml | – name: Information Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, Egypt – name: Torrens University Australia, AUSTRALIA |
| Author_xml | – sequence: 1 givenname: Emad T. orcidid: 0000-0001-5568-2596 surname: Elkabbash fullname: Elkabbash, Emad T. – sequence: 2 givenname: Reham R. surname: Mostafa fullname: Mostafa, Reham R. – sequence: 3 givenname: Sherif I. surname: Barakat fullname: Barakat, Sherif I. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34797851$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1038_s41598_022_23121_z crossref_primary_10_3390_app13042172 crossref_primary_10_1007_s11063_023_11171_x crossref_primary_10_1016_j_asoc_2023_110377 crossref_primary_10_1007_s43538_023_00182_w crossref_primary_10_1007_s00521_022_07842_w crossref_primary_10_1016_j_cie_2022_108508 crossref_primary_10_3390_math11040851 crossref_primary_10_1002_cpe_70054 crossref_primary_10_1109_ACCESS_2024_3415355 |
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| Copyright | COPYRIGHT 2021 Public Library of Science 2021 Elkabbash et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Elkabbash et al 2021 Elkabbash et al |
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| Title | Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer |
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