BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning
Android (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; Selec...
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2023
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my.uniten.dspace-270892023-05-29T17:39:26Z BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning Chimeleze C. Jamil N. Ismail R. Lam K.-Y. Teh J.S. Samual J. Akachukwu Okeke C. 57222127806 36682671900 15839357700 7403657062 56579944200 57216287132 57949004700 Android (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 algorithms Malware 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. Final 2023-05-29T09:39:26Z 2023-05-29T09:39:26Z 2022 Article 10.1155/2022/5339926 2-s2.0-85140988892 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140988892&doi=10.1155%2f2022%2f5339926&partnerID=40&md5=392b7e0a7964ba168b415b6ad80a6d1d https://irepository.uniten.edu.my/handle/123456789/27089 2022 5339926 All Open Access, Gold Hindawi Limited Scopus |
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Android (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 algorithms |
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57222127806 Chimeleze C. Jamil N. Ismail R. Lam K.-Y. Teh J.S. Samual J. Akachukwu Okeke C. |
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Article |
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Chimeleze C. Jamil N. Ismail R. Lam K.-Y. Teh J.S. Samual J. Akachukwu Okeke C. |
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Chimeleze C. Jamil N. Ismail R. Lam K.-Y. Teh J.S. Samual J. Akachukwu Okeke C. BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning |
| author_sort |
Chimeleze C. |
| title |
BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning |
| title_short |
BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning |
| title_full |
BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning |
| title_fullStr |
BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning |
| title_full_unstemmed |
BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning |
| title_sort |
bfedroid: a feature selection technique to detect malware in android apps using machine learning |
| publisher |
Hindawi Limited |
| publishDate |
2023 |
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1806427536356278272 |