Android Malware Detection Using Machine Learning
Malware, or malicious software, poses a significant threat to systems and networks. Malware attacks are becoming extremely sophisticated, and the ability to detect and prevent them is becoming more challenging. Detecting and preventing malware is crucial for several reasons, including the security o...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| التنسيق: | article |
| منشور في: |
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/26288 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513442451292160 |
|---|---|
| author | Al Ali, Shaikha |
| author2 | Suleiman, Ali Hallal, Ghina Alseiari, Sultan Ma, Yiguang Dhou, Salam Aloul, Fadi |
| author2_role | author author author author author author |
| author_facet | Al Ali, Shaikha Suleiman, Ali Hallal, Ghina Alseiari, Sultan Ma, Yiguang Dhou, Salam Aloul, Fadi |
| author_role | author |
| dc.creator.none.fl_str_mv | Al Ali, Shaikha Suleiman, Ali Hallal, Ghina Alseiari, Sultan Ma, Yiguang Dhou, Salam Aloul, Fadi |
| dc.date.none.fl_str_mv | 2024 2025-09-02T05:55:34Z 2025-09-02T05:55:34Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | S. A. Ali et al., "Android Malware Detection Using Machine Learning," 2024 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia, 2024, pp. 79-84, doi: 10.1109/IoTaIS64014.2024.10799339. 2832-1383 https://hdl.handle.net/11073/26288 10.1109/IoTaIS64014.2024.10799339 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE Xplore |
| dc.relation.none.fl_str_mv | https://doi.org/10.1109/IoTaIS64014.2024.10799339 |
| dc.subject.none.fl_str_mv | Support vector machines Performance evaluation Machine learning algorithms Machine learning Nearest neighbor methods Malware Mobile handsets Security Kernel Protection Android Malware detection Goodware Machine learning Cybersecurity |
| dc.title.none.fl_str_mv | Android Malware Detection Using Machine Learning |
| dc.type.none.fl_str_mv | Peer-Reviewed Postprint info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Malware, or malicious software, poses a significant threat to systems and networks. Malware attacks are becoming extremely sophisticated, and the ability to detect and prevent them is becoming more challenging. Detecting and preventing malware is crucial for several reasons, including the security of personal information, data loss and tampering, system disruptions, financial losses, and reputation damage. This paper presents a machine learning approach for Android malware detection. In this work, several machine learning algorithms were utilized, namely k-Nearest neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM) and other ensemble classifiers including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM) and CatBoost. It was found that SVM using radial basis function (RBF) kernel achieved the highest performance with an accuracy of 99.5%. This work proved the feasibility of using machine learning in detecting malware and improving the security of mobile devices. The results of this work can be used to build more robust systems to protect devices and networks from malicious attacks. |
| format | article |
| id | aus_67775c34df97d765a3f38cedeef81add |
| identifier_str_mv | S. A. Ali et al., "Android Malware Detection Using Machine Learning," 2024 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia, 2024, pp. 79-84, doi: 10.1109/IoTaIS64014.2024.10799339. 2832-1383 10.1109/IoTaIS64014.2024.10799339 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/26288 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | IEEE Xplore |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Android Malware Detection Using Machine LearningAl Ali, ShaikhaSuleiman, AliHallal, GhinaAlseiari, SultanMa, YiguangDhou, SalamAloul, FadiSupport vector machinesPerformance evaluationMachine learning algorithmsMachine learningNearest neighbor methodsMalwareMobile handsetsSecurityKernelProtectionAndroidMalware detectionGoodwareMachine learningCybersecurityMalware, or malicious software, poses a significant threat to systems and networks. Malware attacks are becoming extremely sophisticated, and the ability to detect and prevent them is becoming more challenging. Detecting and preventing malware is crucial for several reasons, including the security of personal information, data loss and tampering, system disruptions, financial losses, and reputation damage. This paper presents a machine learning approach for Android malware detection. In this work, several machine learning algorithms were utilized, namely k-Nearest neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM) and other ensemble classifiers including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM) and CatBoost. It was found that SVM using radial basis function (RBF) kernel achieved the highest performance with an accuracy of 99.5%. This work proved the feasibility of using machine learning in detecting malware and improving the security of mobile devices. The results of this work can be used to build more robust systems to protect devices and networks from malicious attacks.IEEE Xplore2025-09-02T05:55:34Z2025-09-02T05:55:34Z2024Peer-ReviewedPostprintinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfS. A. Ali et al., "Android Malware Detection Using Machine Learning," 2024 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Bali, Indonesia, 2024, pp. 79-84, doi: 10.1109/IoTaIS64014.2024.10799339.2832-1383https://hdl.handle.net/11073/2628810.1109/IoTaIS64014.2024.10799339en_UShttps://doi.org/10.1109/IoTaIS64014.2024.10799339oai:repository.aus.edu:11073/262882025-09-03T09:52:43Z |
| spellingShingle | Android Malware Detection Using Machine Learning Al Ali, Shaikha Support vector machines Performance evaluation Machine learning algorithms Machine learning Nearest neighbor methods Malware Mobile handsets Security Kernel Protection Android Malware detection Goodware Machine learning Cybersecurity |
| status_str | publishedVersion |
| title | Android Malware Detection Using Machine Learning |
| title_full | Android Malware Detection Using Machine Learning |
| title_fullStr | Android Malware Detection Using Machine Learning |
| title_full_unstemmed | Android Malware Detection Using Machine Learning |
| title_short | Android Malware Detection Using Machine Learning |
| title_sort | Android Malware Detection Using Machine Learning |
| topic | Support vector machines Performance evaluation Machine learning algorithms Machine learning Nearest neighbor methods Malware Mobile handsets Security Kernel Protection Android Malware detection Goodware Machine learning Cybersecurity |
| url | https://hdl.handle.net/11073/26288 |