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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Autores principales: Emad T Elkabbash, Reham R Mostafa, Sherif I Barakat
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/c796b51367c34edd8d3339774da773a8
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spelling oai:doaj.org-article:c796b51367c34edd8d3339774da773a82021-12-02T20:12:39ZAndroid malware classification based on random vector functional link and artificial Jellyfish Search optimizer.1932-620310.1371/journal.pone.0260232https://doaj.org/article/c796b51367c34edd8d3339774da773a82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0260232https://doaj.org/toc/1932-6203Smartphone 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.Emad T ElkabbashReham R MostafaSherif I BarakatPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0260232 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Emad T Elkabbash
Reham R Mostafa
Sherif I Barakat
Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer.
description 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.
format article
author Emad T Elkabbash
Reham R Mostafa
Sherif I Barakat
author_facet Emad T Elkabbash
Reham R Mostafa
Sherif I Barakat
author_sort Emad T Elkabbash
title Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer.
title_short Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer.
title_full Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer.
title_fullStr Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer.
title_full_unstemmed Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer.
title_sort android malware classification based on random vector functional link and artificial jellyfish search optimizer.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/c796b51367c34edd8d3339774da773a8
work_keys_str_mv AT emadtelkabbash androidmalwareclassificationbasedonrandomvectorfunctionallinkandartificialjellyfishsearchoptimizer
AT rehamrmostafa androidmalwareclassificationbasedonrandomvectorfunctionallinkandartificialjellyfishsearchoptimizer
AT sherifibarakat androidmalwareclassificationbasedonrandomvectorfunctionallinkandartificialjellyfishsearchoptimizer
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