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A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps

The growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware. Traditional detection techniques are often inadequate against increasingly sophisticated malware, prompting this research article...

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Main Authors:	Chimeleze C., Jamil N., Alturki N., Muhammad Zain Z.
Other Authors:	57222127806
Format:	Article
Published:	Elsevier B.V. 2025
Subjects:	Simulated annealing Android apps Android malware Android malware detection C-means Clusterings Fuzzy C-Means clustering Gradient boosting Gradient boosting machine Malware detection Malwares
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id	my.uniten.dspace-36082
record_format	dspace
spelling	my.uniten.dspace-360822025-03-03T15:41:21Z A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps Chimeleze C. Jamil N. Alturki N. Muhammad Zain Z. 57222127806 36682671900 57226667238 59062250200 Simulated annealing Android apps Android malware Android malware detection C-means Clusterings Fuzzy C-Means clustering Gradient boosting Gradient boosting machine Malware detection Malwares Android malware The growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware. Traditional detection techniques are often inadequate against increasingly sophisticated malware, prompting this research article to propose a new clustering method?fuzzy C-mean simulated annealing (FCMSA)?to enhance malware detection through machine learning. The FCMSA clustering technique improves performance by minimizing vulnerabilities, reducing outliers, and optimizing large datasets. The proposed technique selects high-quality clusters from Android app permissions and, using lightGBM, classifies Android malware. Experimental results show that the proposed FCMSA-GBM technique achieves superior accuracy (99.21%) and precision (99.70%) compared to other prevalent cluster-based Android malware detection techniques, while also lowering error rates and execution time. ? 2024 Final 2025-03-03T07:41:21Z 2025-03-03T07:41:21Z 2024 Article 10.1016/j.eij.2024.100560 2-s2.0-85206613550 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206613550&doi=10.1016%2fj.eij.2024.100560&partnerID=40&md5=4127e4f38d77aca50dee8c76f32f1b94 https://irepository.uniten.edu.my/handle/123456789/36082 28 100560 Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Simulated annealing Android apps Android malware Android malware detection C-means Clusterings Fuzzy C-Means clustering Gradient boosting Gradient boosting machine Malware detection Malwares Android malware
spellingShingle	Simulated annealing Android apps Android malware Android malware detection C-means Clusterings Fuzzy C-Means clustering Gradient boosting Gradient boosting machine Malware detection Malwares Android malware Chimeleze C. Jamil N. Alturki N. Muhammad Zain Z. A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps
description	The growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware. Traditional detection techniques are often inadequate against increasingly sophisticated malware, prompting this research article to propose a new clustering method?fuzzy C-mean simulated annealing (FCMSA)?to enhance malware detection through machine learning. The FCMSA clustering technique improves performance by minimizing vulnerabilities, reducing outliers, and optimizing large datasets. The proposed technique selects high-quality clusters from Android app permissions and, using lightGBM, classifies Android malware. Experimental results show that the proposed FCMSA-GBM technique achieves superior accuracy (99.21%) and precision (99.70%) compared to other prevalent cluster-based Android malware detection techniques, while also lowering error rates and execution time. ? 2024
author2	57222127806
author_facet	57222127806 Chimeleze C. Jamil N. Alturki N. Muhammad Zain Z.
format	Article
author	Chimeleze C. Jamil N. Alturki N. Muhammad Zain Z.
author_sort	Chimeleze C.
title	A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps
title_short	A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps
title_full	A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps
title_fullStr	A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps
title_full_unstemmed	A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps
title_sort	lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in android apps
publisher	Elsevier B.V.
publishDate	2025
_version_	1825816262305906688
score	13.244413

A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps

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