A static analysis approach for android permission-based malware detection systems
The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic...
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| Format: | Article |
| Language: | English |
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Public Library of Science
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32478/ http://umpir.ump.edu.my/id/eprint/32478/1/A%20static%20analysis%20approach%20for%20Android%20permission.pdf |
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| author | Juliza, Mohamad Arif Mohd Faizal, Ab Razak Suryanti, Awang Sharfah Ratibah, Tuan Mat Nor Syahidatul Nadiah, Ismail Ahmad Firdaus, Zainal Abidin |
| author_facet | Juliza, Mohamad Arif Mohd Faizal, Ab Razak Suryanti, Awang Sharfah Ratibah, Tuan Mat Nor Syahidatul Nadiah, Ismail Ahmad Firdaus, Zainal Abidin |
| author_sort | Juliza, Mohamad Arif |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic analysis, which monitors malware behaviour. While both perform security evaluations successfully, there is still room for improvement. The goal of this research is to examine the effectiveness of static analysis to detect Android malware by using permission-based features. This study proposes machine learning with different sets of classifiers was used to evaluate Android malware detection. The feature selection method in this study was applied to determine which features were most capable of distinguishing malware. A total of 5,000 Drebin malware samples and 5,000 Androzoo benign samples were utilised. The performances of the different sets of classifiers were then compared. The results indicated that with a TPR value of 91.6%, the Random Forest algorithm achieved the highest level of accuracy in malware detection. |
| first_indexed | 2025-11-15T03:06:33Z |
| format | Article |
| id | ump-32478 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:06:33Z |
| publishDate | 2021 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-324782024-01-04T01:38:33Z http://umpir.ump.edu.my/id/eprint/32478/ A static analysis approach for android permission-based malware detection systems Juliza, Mohamad Arif Mohd Faizal, Ab Razak Suryanti, Awang Sharfah Ratibah, Tuan Mat Nor Syahidatul Nadiah, Ismail Ahmad Firdaus, Zainal Abidin QA76 Computer software The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic analysis, which monitors malware behaviour. While both perform security evaluations successfully, there is still room for improvement. The goal of this research is to examine the effectiveness of static analysis to detect Android malware by using permission-based features. This study proposes machine learning with different sets of classifiers was used to evaluate Android malware detection. The feature selection method in this study was applied to determine which features were most capable of distinguishing malware. A total of 5,000 Drebin malware samples and 5,000 Androzoo benign samples were utilised. The performances of the different sets of classifiers were then compared. The results indicated that with a TPR value of 91.6%, the Random Forest algorithm achieved the highest level of accuracy in malware detection. Public Library of Science 2021-09-30 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/32478/1/A%20static%20analysis%20approach%20for%20Android%20permission.pdf Juliza, Mohamad Arif and Mohd Faizal, Ab Razak and Suryanti, Awang and Sharfah Ratibah, Tuan Mat and Nor Syahidatul Nadiah, Ismail and Ahmad Firdaus, Zainal Abidin (2021) A static analysis approach for android permission-based malware detection systems. PLoS ONE, 16 (9). pp. 1-23. ISSN 1932-6203. (Published) https://doi.org/10.1371/journal.pone.0257968 https://doi.org/10.1371/journal.pone.0257968 |
| spellingShingle | QA76 Computer software Juliza, Mohamad Arif Mohd Faizal, Ab Razak Suryanti, Awang Sharfah Ratibah, Tuan Mat Nor Syahidatul Nadiah, Ismail Ahmad Firdaus, Zainal Abidin A static analysis approach for android permission-based malware detection systems |
| title | A static analysis approach for android permission-based malware detection systems |
| title_full | A static analysis approach for android permission-based malware detection systems |
| title_fullStr | A static analysis approach for android permission-based malware detection systems |
| title_full_unstemmed | A static analysis approach for android permission-based malware detection systems |
| title_short | A static analysis approach for android permission-based malware detection systems |
| title_sort | static analysis approach for android permission-based malware detection systems |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/32478/ http://umpir.ump.edu.my/id/eprint/32478/ http://umpir.ump.edu.my/id/eprint/32478/ http://umpir.ump.edu.my/id/eprint/32478/1/A%20static%20analysis%20approach%20for%20Android%20permission.pdf |