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
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/03e55aa1d8f94389a1a21e104f7a730b
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id	oai:doaj.org-article:03e55aa1d8f94389a1a21e104f7a730b
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spelling	oai:doaj.org-article:03e55aa1d8f94389a1a21e104f7a730b2021-11-25T06:19:30ZAndroid malware classification based on random vector functional link and artificial Jellyfish Search optimizer1932-6203https://doaj.org/article/03e55aa1d8f94389a1a21e104f7a730b2021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604294/?tool=EBIhttps://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 (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/03e55aa1d8f94389a1a21e104f7a730b
work_keys_str_mv	AT emadtelkabbash androidmalwareclassificationbasedonrandomvectorfunctionallinkandartificialjellyfishsearchoptimizer AT rehamrmostafa androidmalwareclassificationbasedonrandomvectorfunctionallinkandartificialjellyfishsearchoptimizer AT sherifibarakat androidmalwareclassificationbasedonrandomvectorfunctionallinkandartificialjellyfishsearchoptimizer
_version_	1718413843423559680

Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer

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