Discovering optimal features using static analysis and a genetic search based method for Android malware detection
Mobile device manufacturers are rapidly producing miscellaneous android versions worldwide. Simultaneously, cyber criminals are executing malicious actions such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as many people use a...
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| Format: | Article |
| Language: | English English |
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Springer
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/1/Discovering%20optimal%20features%20using%20static.pdf http://umpir.ump.edu.my/id/eprint/19177/2/Discovering%20optimal%20features%20using%20static1.pdf |
| _version_ | 1848820847271215104 |
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| author | Ahmad Firdaus, Zainal Abidin Nor Badrul, Anuar Ahmad, Karim Mohd Faizal, Ab Razak |
| author_facet | Ahmad Firdaus, Zainal Abidin Nor Badrul, Anuar Ahmad, Karim Mohd Faizal, Ab Razak |
| author_sort | Ahmad Firdaus, Zainal Abidin |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Mobile device manufacturers are rapidly producing miscellaneous android versions worldwide. Simultaneously, cyber criminals are executing malicious actions such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as many people use android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. In this study, we used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimal number of features to classify malware efficiently. Therefore, we used genetic search (GS), which is a search based on a genetic algorithm (GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), Functional Trees (FT), J48, Random Forest (RF), and Multilayer Perceptron (MLP). Among these classifiers, FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use of only six features. |
| first_indexed | 2025-11-15T02:15:57Z |
| format | Article |
| id | ump-19177 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:15:57Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-191772018-08-10T03:29:39Z http://umpir.ump.edu.my/id/eprint/19177/ Discovering optimal features using static analysis and a genetic search based method for Android malware detection Ahmad Firdaus, Zainal Abidin Nor Badrul, Anuar Ahmad, Karim Mohd Faizal, Ab Razak QA75 Electronic computers. Computer science Mobile device manufacturers are rapidly producing miscellaneous android versions worldwide. Simultaneously, cyber criminals are executing malicious actions such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as many people use android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. In this study, we used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimal number of features to classify malware efficiently. Therefore, we used genetic search (GS), which is a search based on a genetic algorithm (GA), to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Naïve Bayes (NB), Functional Trees (FT), J48, Random Forest (RF), and Multilayer Perceptron (MLP). Among these classifiers, FT gave the highest accuracy (95%) and true positive rate (TPR) (96.7%) with the use of only six features. Springer 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19177/1/Discovering%20optimal%20features%20using%20static.pdf pdf en http://umpir.ump.edu.my/id/eprint/19177/2/Discovering%20optimal%20features%20using%20static1.pdf Ahmad Firdaus, Zainal Abidin and Nor Badrul, Anuar and Ahmad, Karim and Mohd Faizal, Ab Razak (2018) Discovering optimal features using static analysis and a genetic search based method for Android malware detection. Frontiers of Information Technology & Electronic Engineering, 19 (6). pp. 712-736. ISSN 2095-9230. (Published) https://doi.org/10.1631/FITEE.1601491 doi: 10.1631/FITEE.1601491 |
| spellingShingle | QA75 Electronic computers. Computer science Ahmad Firdaus, Zainal Abidin Nor Badrul, Anuar Ahmad, Karim Mohd Faizal, Ab Razak Discovering optimal features using static analysis and a genetic search based method for Android malware detection |
| title | Discovering optimal features using static analysis and a genetic search based method for Android malware detection |
| title_full | Discovering optimal features using static analysis and a genetic search based method for Android malware detection |
| title_fullStr | Discovering optimal features using static analysis and a genetic search based method for Android malware detection |
| title_full_unstemmed | Discovering optimal features using static analysis and a genetic search based method for Android malware detection |
| title_short | Discovering optimal features using static analysis and a genetic search based method for Android malware detection |
| title_sort | discovering optimal features using static analysis and a genetic search based method for android malware detection |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/ http://umpir.ump.edu.my/id/eprint/19177/1/Discovering%20optimal%20features%20using%20static.pdf http://umpir.ump.edu.my/id/eprint/19177/2/Discovering%20optimal%20features%20using%20static1.pdf |