Android Malware Category and Family Identification Using Parallel Machine Learning
Android malware is one of the most dangerous threats on the Internet. It has been on the rise for several years. As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc. One of the most growing attacks is on Android systems due to it...
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| Format: | Article |
| Language: | English |
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University of Tehran
2022-07-01
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| Series: | Journal of Information Technology Management |
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| Online Access: | https://jitm.ut.ac.ir/article_88133_16d42429ea8c150b3d16ef50fe0a21d7.pdf |
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| _version_ | 1849325043460341760 |
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| author | Ahmed Hashem El Fiky Mohamed Ashraf Madkour Ayman El Shenawy |
| author_facet | Ahmed Hashem El Fiky Mohamed Ashraf Madkour Ayman El Shenawy |
| author_sort | Ahmed Hashem El Fiky |
| collection | DOAJ |
| description | Android malware is one of the most dangerous threats on the Internet. It has been on the rise for several years. As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc. One of the most growing attacks is on Android systems due to its use in many devices worldwide. De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance. There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Hence, understand the distinct ob-jective of malware after identifying their family and category. This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families. Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families. The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work. The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods. The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware. |
| format | Article |
| id | doaj-art-a34b69bcf2b0428587a2d7da74312067 |
| institution | Kabale University |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-a34b69bcf2b0428587a2d7da743120672025-08-20T03:48:31ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592022-07-01144193910.22059/jitm.2022.8813388133Android Malware Category and Family Identification Using Parallel Machine LearningAhmed Hashem El Fiky0Mohamed Ashraf Madkour1Ayman El Shenawy2M.Sc. in Systems and Computers Engineering, Department of Systems and Computers Engineering, Faculty of Engineering Al-Azhar University, Cairo, Egypt.Professor, Department of Systems and Computers Engineering, Faculty of Engineering Al-Azhar University, Cairo, Egypt.Assistant Professor, Department of Systems and Computers Engineering, Faculty of Engineering Al-Azhar University, Cairo, Egypt; Software Engineering and Information Technology, Faculty of Engineering and technology, Egyptian Chinese University, Cairo, Egypt.Android malware is one of the most dangerous threats on the Internet. It has been on the rise for several years. As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc. One of the most growing attacks is on Android systems due to its use in many devices worldwide. De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance. There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Hence, understand the distinct ob-jective of malware after identifying their family and category. This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families. Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families. The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work. The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods. The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware.https://jitm.ut.ac.ir/article_88133_16d42429ea8c150b3d16ef50fe0a21d7.pdfandroid malwaremalware analysismalware category classificationmalware family classificationmalware dynamic analysis |
| spellingShingle | Ahmed Hashem El Fiky Mohamed Ashraf Madkour Ayman El Shenawy Android Malware Category and Family Identification Using Parallel Machine Learning Journal of Information Technology Management android malware malware analysis malware category classification malware family classification malware dynamic analysis |
| title | Android Malware Category and Family Identification Using Parallel Machine Learning |
| title_full | Android Malware Category and Family Identification Using Parallel Machine Learning |
| title_fullStr | Android Malware Category and Family Identification Using Parallel Machine Learning |
| title_full_unstemmed | Android Malware Category and Family Identification Using Parallel Machine Learning |
| title_short | Android Malware Category and Family Identification Using Parallel Machine Learning |
| title_sort | android malware category and family identification using parallel machine learning |
| topic | android malware malware analysis malware category classification malware family classification malware dynamic analysis |
| url | https://jitm.ut.ac.ir/article_88133_16d42429ea8c150b3d16ef50fe0a21d7.pdf |
| work_keys_str_mv | AT ahmedhashemelfiky androidmalwarecategoryandfamilyidentificationusingparallelmachinelearning AT mohamedashrafmadkour androidmalwarecategoryandfamilyidentificationusingparallelmachinelearning AT aymanelshenawy androidmalwarecategoryandfamilyidentificationusingparallelmachinelearning |