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BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

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dc.contributor.authorChimeleze C.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorIsmail R.en_US
dc.contributor.authorLam K.-Y.en_US
dc.contributor.authorTeh J.S.en_US
dc.contributor.authorSamual J.en_US
dc.contributor.authorAkachukwu Okeke C.en_US
dc.contributor.authorid57222127806en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid15839357700en_US
dc.contributor.authorid7403657062en_US
dc.contributor.authorid56579944200en_US
dc.contributor.authorid57216287132en_US
dc.contributor.authorid57949004700en_US
dc.date.accessioned2023-05-29T09:39:26Z
dc.date.available2023-05-29T09:39:26Z
dc.date.issued2022
dc.descriptionAndroid (operating system); Android malware; Classification (of information); Feature Selection; Learning systems; Mobile security; Android apps; Classification models; Feature weight; Features selection; Machine learning algorithms; Machine-learning; Malware detection; Malwares; Memory usage; Selection techniques; Learning algorithmsen_US
dc.description.abstractMalware detection refers to the process of detecting the presence of malware on a host system, or that of determining whether a specific program is malicious or benign. Machine learning-based solutions first gather information from applications and then use machine learning algorithms to develop a classifier that can distinguish between malicious and benign applications. Researchers and practitioners have long paid close attention to the issue. Most previous work has addressed the differences in feature importance or the computation of feature weights, which is unrelated to the classification model used, and therefore, the implementation of a selection approach with limited feature hiccups, and increases the execution time and memory usage. BFEDroid is a machine learning detection strategy that combines backward, forward, and exhaustive subset selection. This proposed malware detection technique can be updated by retraining new applications with true labels. It has higher accuracy (99%), lower memory consumption (1680), and a shorter execution time (1.264SI) than current malware detection methods that use feature selection. � 2022 Collins Chimeleze et al.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5339926
dc.identifier.doi10.1155/2022/5339926
dc.identifier.scopus2-s2.0-85140988892
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85140988892&doi=10.1155%2f2022%2f5339926&partnerID=40&md5=392b7e0a7964ba168b415b6ad80a6d1d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27089
dc.identifier.volume2022
dc.publisherHindawi Limiteden_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleSecurity and Communication Networks
dc.titleBFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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