الفهرس 
INTRODUCTION AND OBJECTIVESOnly 14 pages are availabe for public view
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Abstract
”Smartphone usage is nearly ubiquitous worldwide, and Android is the leading open-source operating system (OS), securing the most significant market share and active user population. the popularity of free Android applications has risen rapidly. This has led to the unintended installation of malicious Android apps that violate user privacy or conduct attacks. Malware detection on Android platforms is therefore growing due to the unwanted similarity between malicious behavior and benign behavior which can lead to slow detection and compromise on infected phones for relatively lengthy periods of time. Hence, attackers target the Android OS to capitalize on consumer reliance and vulnerability. Hackers often use confidential user data to exploit them 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 a swarm-based metaheuristic algorithm called 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 against compared methods and studies used same dataset by 98.41%. RVFL+JS minimizes the runtime of execution of optimized models to preserve the best performance metrics using a dataset consisting of 11,598 multi-class applications and 470 static and dynamic features ”