Analysis of Data Mining Tools for Android Malware Detection
There are various data mining tools available to analyze data related android malware detection. However, the problem arises in deciding the most appropriate machine learning techniques or algorithm on particular tools to be implemented on particular data. This research is focusing only on classifi...
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| Format: | Article |
| Language: | en |
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Faculty Of Information And Communication Technology, UTeM
2019
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| Online Access: | http://eprints.utem.edu.my/id/eprint/24018/1/http%3A//portal.utem.edu.my/iURIS/uploadfile/Journal/00763/Analysis%20of%20Data%20mining%20tools%20for%20Android%20Malware%20Detections.pdf http://eprints.utem.edu.my/id/eprint/24018/ https://jacta.utem.edu.my/jacta/article/view/5196 |
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| author | Yusof, Robiah Abdullah, Raihana Syahirah Adnan, Nurul Syahirrah Abd. Jalil, Nurlaily |
| author_facet | Yusof, Robiah Abdullah, Raihana Syahirah Adnan, Nurul Syahirrah Abd. Jalil, Nurlaily |
| author_sort | Yusof, Robiah |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | There are various data mining tools available to
analyze data related android malware detection. However, the problem arises in deciding the most appropriate machine learning techniques or algorithm on particular tools to be implemented on particular data. This research is focusing only on classification techniques. Hence, the objective of this research is to identify the best machine learning technique or algorithm on selected tool for android malware detection. Five techniques: Random Forest, Naive Bayes, Support Vector Machine, Forest, K-Nearest Neighbour and Adaboost are selected and applied in selected tools namely Weka and Orange. The result shows that Adaboost technique in Weka tool and Random Forest technique in Orange tool has obtained accuracy above 80% compare to other
techniques. This result provides an option for the researcher on applying technique or algorithm on selected tool when analyzing android malware data. |
| format | Article |
| id | my.utem.eprints-24018 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2019 |
| publisher | Faculty Of Information And Communication Technology, UTeM |
| record_format | eprints |
| spelling | my.utem.eprints-240182022-03-22T11:13:45Z http://eprints.utem.edu.my/id/eprint/24018/ Analysis of Data Mining Tools for Android Malware Detection Yusof, Robiah Abdullah, Raihana Syahirah Adnan, Nurul Syahirrah Abd. Jalil, Nurlaily There are various data mining tools available to analyze data related android malware detection. However, the problem arises in deciding the most appropriate machine learning techniques or algorithm on particular tools to be implemented on particular data. This research is focusing only on classification techniques. Hence, the objective of this research is to identify the best machine learning technique or algorithm on selected tool for android malware detection. Five techniques: Random Forest, Naive Bayes, Support Vector Machine, Forest, K-Nearest Neighbour and Adaboost are selected and applied in selected tools namely Weka and Orange. The result shows that Adaboost technique in Weka tool and Random Forest technique in Orange tool has obtained accuracy above 80% compare to other techniques. This result provides an option for the researcher on applying technique or algorithm on selected tool when analyzing android malware data. Faculty Of Information And Communication Technology, UTeM 2019-11 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/24018/1/http%3A//portal.utem.edu.my/iURIS/uploadfile/Journal/00763/Analysis%20of%20Data%20mining%20tools%20for%20Android%20Malware%20Detections.pdf Yusof, Robiah and Abdullah, Raihana Syahirah and Adnan, Nurul Syahirrah and Abd. Jalil, Nurlaily (2019) Analysis of Data Mining Tools for Android Malware Detection. Journal Of Advanced Computing Technology And Application (JACTA), 1 (2). pp. 22-26. ISSN 2672-7188 https://jacta.utem.edu.my/jacta/article/view/5196 |
| spellingShingle | Yusof, Robiah Abdullah, Raihana Syahirah Adnan, Nurul Syahirrah Abd. Jalil, Nurlaily Analysis of Data Mining Tools for Android Malware Detection |
| title | Analysis of Data Mining Tools for Android Malware Detection |
| title_full | Analysis of Data Mining Tools for Android Malware Detection |
| title_fullStr | Analysis of Data Mining Tools for Android Malware Detection |
| title_full_unstemmed | Analysis of Data Mining Tools for Android Malware Detection |
| title_short | Analysis of Data Mining Tools for Android Malware Detection |
| title_sort | analysis of data mining tools for android malware detection |
| url | http://eprints.utem.edu.my/id/eprint/24018/1/http%3A//portal.utem.edu.my/iURIS/uploadfile/Journal/00763/Analysis%20of%20Data%20mining%20tools%20for%20Android%20Malware%20Detections.pdf http://eprints.utem.edu.my/id/eprint/24018/ https://jacta.utem.edu.my/jacta/article/view/5196 |
| url_provider | http://eprints.utem.edu.my/ |