Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection
Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for An...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
World Scientific Publishing
2020-05-01
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| Series: | Vietnam Journal of Computer Science |
| Subjects: | |
| Online Access: | http://www.worldscientific.com/doi/pdf/10.1142/S2196888820500086 |
| _version_ | 1828318193412734976 |
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| author | Md. Shohel Rana Andrew H. Sung |
| author_facet | Md. Shohel Rana Andrew H. Sung |
| author_sort | Md. Shohel Rana |
| collection | DOAJ |
| description | Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection. |
| first_indexed | 2024-04-13T17:38:33Z |
| format | Article |
| id | doaj.art-1eda8e3ae7b54e6db03ebb05759c0d8d |
| institution | Directory Open Access Journal |
| issn | 2196-8888 2196-8896 |
| language | English |
| last_indexed | 2024-04-13T17:38:33Z |
| publishDate | 2020-05-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Vietnam Journal of Computer Science |
| spelling | doaj.art-1eda8e3ae7b54e6db03ebb05759c0d8d2022-12-22T02:37:15ZengWorld Scientific PublishingVietnam Journal of Computer Science2196-88882196-88962020-05-017214515910.1142/S219688882050008610.1142/S2196888820500086Evaluation of Advanced Ensemble Learning Techniques for Android Malware DetectionMd. Shohel Rana0Andrew H. Sung1School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, 118 College Drive, #5106 Hattiesburg, MS 39406, USASchool of Computing Sciences and Computer Engineering, The University of Southern Mississippi, 118 College Drive, #5106 Hattiesburg, MS 39406, USAAndroid is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.http://www.worldscientific.com/doi/pdf/10.1142/S2196888820500086ensemble learningdrebinsbfsmalwarestackingblendingbaggingboosting |
| spellingShingle | Md. Shohel Rana Andrew H. Sung Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection Vietnam Journal of Computer Science ensemble learning drebin sbfs malware stacking blending bagging boosting |
| title | Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection |
| title_full | Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection |
| title_fullStr | Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection |
| title_full_unstemmed | Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection |
| title_short | Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection |
| title_sort | evaluation of advanced ensemble learning techniques for android malware detection |
| topic | ensemble learning drebin sbfs malware stacking blending bagging boosting |
| url | http://www.worldscientific.com/doi/pdf/10.1142/S2196888820500086 |
| work_keys_str_mv | AT mdshohelrana evaluationofadvancedensemblelearningtechniquesforandroidmalwaredetection AT andrewhsung evaluationofadvancedensemblelearningtechniquesforandroidmalwaredetection |