CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES
Android malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking. Therefore, understanding the unique behaviors of common Android malware categories is essential. This study util...
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| Format: | Article |
| Language: | English |
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Faculty of Engineering, University of Kufa
2025-04-01
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| Series: | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
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| Online Access: | https://journal.uokufa.edu.iq/index.php/kje/article/view/16526 |
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| author | abdullah alsraratee Ahmed Al-Azawei |
| author_facet | abdullah alsraratee Ahmed Al-Azawei |
| author_sort | abdullah alsraratee |
| collection | DOAJ |
| description | Android malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking. Therefore, understanding the unique behaviors of common Android malware categories is essential. This study utilizes machine learning techniques namely, K-Nearest Neighbor, Random Forest and Decision Tree to classify Android malware based on dynamic analysis. As feature selection and reduction techniques, Mutual Information and Principle Component Analysis are used. The research analyzes a large dataset, containing fourteen primary malware categories using the CCCS-CIC-AndMal2020 dataset. Unlike previous research, the proposed method makes a balance between the number of features and classifiers’ performance, resulting in an overall detection accuracy of 98% in the fourteen analyzed categories and excluding 78.87% of the original dataset’s features. The research, thus, introduces an efficient Android malware detection method that reduces the computational cost and improves the classification accuracy. |
| format | Article |
| id | doaj-art-3c558de91a3843a8abc1cda4dea90e2b |
| institution | OA Journals |
| issn | 2071-5528 2523-0018 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Faculty of Engineering, University of Kufa |
| record_format | Article |
| series | Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ |
| spelling | doaj-art-3c558de91a3843a8abc1cda4dea90e2b2025-08-20T02:12:49ZengFaculty of Engineering, University of KufaMağallaẗ Al-kūfaẗ Al-handasiyyaẗ2071-55282523-00182025-04-0116029611810.30572/2018/KJE/160206CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUESabdullah alsraratee0https://orcid.org/0009-0000-6565-5612Ahmed Al-Azawei1https://orcid.org/0000-0002-4121-2531College of Information Technology, University of Babylon, Babil, IraqCollege of Information Technology, University of Babylon, Babil, IraqAndroid malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking. Therefore, understanding the unique behaviors of common Android malware categories is essential. This study utilizes machine learning techniques namely, K-Nearest Neighbor, Random Forest and Decision Tree to classify Android malware based on dynamic analysis. As feature selection and reduction techniques, Mutual Information and Principle Component Analysis are used. The research analyzes a large dataset, containing fourteen primary malware categories using the CCCS-CIC-AndMal2020 dataset. Unlike previous research, the proposed method makes a balance between the number of features and classifiers’ performance, resulting in an overall detection accuracy of 98% in the fourteen analyzed categories and excluding 78.87% of the original dataset’s features. The research, thus, introduces an efficient Android malware detection method that reduces the computational cost and improves the classification accuracy.https://journal.uokufa.edu.iq/index.php/kje/article/view/16526androidmalwaredynamic analysismachine learningmalware category classification |
| spellingShingle | abdullah alsraratee Ahmed Al-Azawei CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ android malware dynamic analysis machine learning malware category classification |
| title | CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES |
| title_full | CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES |
| title_fullStr | CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES |
| title_full_unstemmed | CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES |
| title_short | CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES |
| title_sort | classifying android malware categories based on dynamic features an integration of feature reduction and selection techniques |
| topic | android malware dynamic analysis machine learning malware category classification |
| url | https://journal.uokufa.edu.iq/index.php/kje/article/view/16526 |
| work_keys_str_mv | AT abdullahalsraratee classifyingandroidmalwarecategoriesbasedondynamicfeaturesanintegrationoffeaturereductionandselectiontechniques AT ahmedalazawei classifyingandroidmalwarecategoriesbasedondynamicfeaturesanintegrationoffeaturereductionandselectiontechniques |