Maldroid- attribute selection analysis for malware classification
Android is the most dominant operating system in the mobile market and the number of Android users is increasing year by year. Malware authors use android market as a hub for malicious apps and spread malware to users with the intention to threaten privacy; and this has remained undetected due to th...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JATIT
2019
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38094/ http://umpir.ump.edu.my/id/eprint/38094/1/Maldroid-%20attribute%20selection%20analysis%20for%20malware%20classification.pdf |
| _version_ | 1848825419353030656 |
|---|---|
| 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 | UMP Institutional Repository |
| collection | Online Access |
| description | Android is the most dominant operating system in the mobile market and the number of Android users is increasing year by year. Malware authors use android market as a hub for malicious apps and spread malware to users with the intention to threaten privacy; and this has remained undetected due to the weakness in signature-based detection. A major problem with malware detection is the existence of numerous features in malware code and the need to look at the relevant features in malware analysis. As a result, applying any security solution in malware analysis is considered inefficient because mobile devices have limited resources in terms of its memory, processor and storage. Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. The experiment evaluated 8000 real data samples and the result showed that InfoGainEval and KNN algorithm
are the most selected in attribute selection and classification process. |
| first_indexed | 2025-11-15T03:28:37Z |
| format | Article |
| id | ump-38094 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:28:37Z |
| publishDate | 2019 |
| publisher | JATIT |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-380942023-07-20T03:28:26Z http://umpir.ump.edu.my/id/eprint/38094/ Maldroid- attribute selection analysis for malware classification Rahiwan Nazar, Romli Mohamad Fadli, Zolkipli Mohd Zamri, Osman QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Android is the most dominant operating system in the mobile market and the number of Android users is increasing year by year. Malware authors use android market as a hub for malicious apps and spread malware to users with the intention to threaten privacy; and this has remained undetected due to the weakness in signature-based detection. A major problem with malware detection is the existence of numerous features in malware code and the need to look at the relevant features in malware analysis. As a result, applying any security solution in malware analysis is considered inefficient because mobile devices have limited resources in terms of its memory, processor and storage. Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. The experiment evaluated 8000 real data samples and the result showed that InfoGainEval and KNN algorithm are the most selected in attribute selection and classification process. JATIT 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38094/1/Maldroid-%20attribute%20selection%20analysis%20for%20malware%20classification.pdf Rahiwan Nazar, Romli and Mohamad Fadli, Zolkipli and Mohd Zamri, Osman (2019) Maldroid- attribute selection analysis for malware classification. Journal of Theoretical and Applied Information Technology, 97 (20). pp. 2419-2429. ISSN 1992-8645 (print); 817-3195 (online). (Published) http://www.jatit.org/volumes/Vol97No20/15Vol97No20.pdf |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Rahiwan Nazar, Romli Mohamad Fadli, Zolkipli Mohd Zamri, Osman Maldroid- attribute selection analysis for malware classification |
| title | Maldroid- attribute selection analysis for malware classification |
| title_full | Maldroid- attribute selection analysis for malware classification |
| title_fullStr | Maldroid- attribute selection analysis for malware classification |
| title_full_unstemmed | Maldroid- attribute selection analysis for malware classification |
| title_short | Maldroid- attribute selection analysis for malware classification |
| title_sort | maldroid- attribute selection analysis for malware classification |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/38094/ http://umpir.ump.edu.my/id/eprint/38094/ http://umpir.ump.edu.my/id/eprint/38094/1/Maldroid-%20attribute%20selection%20analysis%20for%20malware%20classification.pdf |