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

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dc.citedby0
dc.contributor.authorChimeleze C.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorAlturki N.en_US
dc.contributor.authorMuhammad Zain Z.en_US
dc.contributor.authorid57222127806en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid57226667238en_US
dc.contributor.authorid59062250200en_US
dc.date.accessioned2025-03-03T07:41:21Z
dc.date.available2025-03-03T07:41:21Z
dc.date.issued2024
dc.description.abstractThe 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. ? 2024en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo100560
dc.identifier.doi10.1016/j.eij.2024.100560
dc.identifier.scopus2-s2.0-85206613550
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85206613550&doi=10.1016%2fj.eij.2024.100560&partnerID=40&md5=4127e4f38d77aca50dee8c76f32f1b94
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36082
dc.identifier.volume28
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleEgyptian Informatics Journal
dc.subjectSimulated annealing
dc.subjectAndroid apps
dc.subjectAndroid malware
dc.subjectAndroid malware detection
dc.subjectC-means
dc.subjectClusterings
dc.subjectFuzzy C-Means clustering
dc.subjectGradient boosting
dc.subjectGradient boosting machine
dc.subjectMalware detection
dc.subjectMalwares
dc.subjectAndroid malware
dc.titleA Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android appsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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