Efficient feature selection analysis for accuracy malware classification
Android is designed for mobile devices and its open-source software. The growth and popularity of android platform are high compared to another platform. Due to its glory, the number of malware has been increasing exponentially. Android system used a permission mechanism to allow users and developer...
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| Format: | Conference or Workshop Item |
| Language: | en |
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IOP Publishing
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31984/1/Efficient%20feature%20selection%20analysis%20for%20accuracy%20malware%20classification.pdf http://umpir.ump.edu.my/id/eprint/31984/ https://doi.org/10.1088/1742-6596/1918/4/042140 |
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| author | Rahiwan Nazar, Romli Mohamad Fadli, Zolkipli Mohd Zamri, Osman |
| author_facet | Rahiwan Nazar, Romli Mohamad Fadli, Zolkipli Mohd Zamri, Osman |
| author_sort | Rahiwan Nazar, Romli |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Android is designed for mobile devices and its open-source software. The growth and popularity of android platform are high compared to another platform. Due to its glory, the number of malware has been increasing exponentially. Android system used a permission mechanism to allow users and developers to manage their access to private information, system resources, and data storage required by Android applications (apps). It became an advantage to an attacker to violent the data. This paper proposes a novel framework for Android malware detection. Our framework used three major methods for effective feature representation on malware detection and used this method to classify malware and benign. The result demonstrates that the Random forest is with 23 features is more accurate detection than the other machine learning algorithm. |
| format | Conference or Workshop Item |
| id | my.ump.umpir.31984 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2021 |
| publisher | IOP Publishing |
| record_format | eprints |
| spelling | my.ump.umpir.319842022-02-11T07:18:08Z http://umpir.ump.edu.my/id/eprint/31984/ Efficient feature selection analysis for accuracy malware classification Rahiwan Nazar, Romli Mohamad Fadli, Zolkipli Mohd Zamri, Osman QA76 Computer software Android is designed for mobile devices and its open-source software. The growth and popularity of android platform are high compared to another platform. Due to its glory, the number of malware has been increasing exponentially. Android system used a permission mechanism to allow users and developers to manage their access to private information, system resources, and data storage required by Android applications (apps). It became an advantage to an attacker to violent the data. This paper proposes a novel framework for Android malware detection. Our framework used three major methods for effective feature representation on malware detection and used this method to classify malware and benign. The result demonstrates that the Random forest is with 23 features is more accurate detection than the other machine learning algorithm. IOP Publishing 2021-06-14 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/31984/1/Efficient%20feature%20selection%20analysis%20for%20accuracy%20malware%20classification.pdf Rahiwan Nazar, Romli and Mohamad Fadli, Zolkipli and Mohd Zamri, Osman (2021) Efficient feature selection analysis for accuracy malware classification. In: Journal of Physics: Conference Series; 7th International Conference on Mathematics, Science, and Education 2020, ICMSE 2020 , 6 October 2020 , Semarang, Virtual. pp. 1-9., 1918 (4). ISSN 1742-6588 (print); 1742-6596 (online) (Published) https://doi.org/10.1088/1742-6596/1918/4/042140 |
| spellingShingle | QA76 Computer software Rahiwan Nazar, Romli Mohamad Fadli, Zolkipli Mohd Zamri, Osman Efficient feature selection analysis for accuracy malware classification |
| title | Efficient feature selection analysis for accuracy malware classification |
| title_full | Efficient feature selection analysis for accuracy malware classification |
| title_fullStr | Efficient feature selection analysis for accuracy malware classification |
| title_full_unstemmed | Efficient feature selection analysis for accuracy malware classification |
| title_short | Efficient feature selection analysis for accuracy malware classification |
| title_sort | efficient feature selection analysis for accuracy malware classification |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/31984/1/Efficient%20feature%20selection%20analysis%20for%20accuracy%20malware%20classification.pdf http://umpir.ump.edu.my/id/eprint/31984/ https://doi.org/10.1088/1742-6596/1918/4/042140 |
| url_provider | http://umpir.ump.edu.my/ |