A Survey on Android Malware Detection Techniques Using Supervised Machine Learning
Android’s open-source nature has contributed to the platform’s rapid growth and its widespread adoption. However, this widespread adoption of the Android operating system (OS) has also attracted the attention of malicious actors who develop malware targeting these devices. Andr...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10734108/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850162035353976832 |
|---|---|
| author | Safa J. Altaha Ahmed Aljughaiman Sonia Gul |
| author_facet | Safa J. Altaha Ahmed Aljughaiman Sonia Gul |
| author_sort | Safa J. Altaha |
| collection | DOAJ |
| description | Android’s open-source nature has contributed to the platform’s rapid growth and its widespread adoption. However, this widespread adoption of the Android operating system (OS) has also attracted the attention of malicious actors who develop malware targeting these devices. Android malware threatens users’ privacy, data security, and overall device performance. Machine learning (ML) plays a significant role in malware analysis and detection because it can process huge amounts of data, identify complex patterns, and adjust to changing threats. The purpose of this paper is to provide a comprehensive review of the existing research on ML-based techniques used to detect and analyze Android malware. In this paper, the security weaknesses in Android OS are explored and the reasons why these weaknesses do not exist in the iPhone operating system (iOS) are discussed. Further, the authors examine the existing studies that have been proposed by researchers and outlines their strengths and limitations. The findings reveal that the existing researches utilize different ML models, features, and detection techniques, including static, dynamic, and hybrid approaches. Moreover, directions for future research and potential areas that require more attention and improvement in this field are highlighted. |
| format | Article |
| id | doaj-art-4ebe23adb86c46f99ed08f768bf923e0 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4ebe23adb86c46f99ed08f768bf923e02025-08-20T02:22:40ZengIEEEIEEE Access2169-35362024-01-011217316817319110.1109/ACCESS.2024.348570610734108A Survey on Android Malware Detection Techniques Using Supervised Machine LearningSafa J. Altaha0https://orcid.org/0009-0001-5751-7639Ahmed Aljughaiman1https://orcid.org/0000-0001-9176-9453Sonia Gul2https://orcid.org/0000-0002-5300-3040Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi ArabiaDepartment of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi ArabiaAndroid’s open-source nature has contributed to the platform’s rapid growth and its widespread adoption. However, this widespread adoption of the Android operating system (OS) has also attracted the attention of malicious actors who develop malware targeting these devices. Android malware threatens users’ privacy, data security, and overall device performance. Machine learning (ML) plays a significant role in malware analysis and detection because it can process huge amounts of data, identify complex patterns, and adjust to changing threats. The purpose of this paper is to provide a comprehensive review of the existing research on ML-based techniques used to detect and analyze Android malware. In this paper, the security weaknesses in Android OS are explored and the reasons why these weaknesses do not exist in the iPhone operating system (iOS) are discussed. Further, the authors examine the existing studies that have been proposed by researchers and outlines their strengths and limitations. The findings reveal that the existing researches utilize different ML models, features, and detection techniques, including static, dynamic, and hybrid approaches. Moreover, directions for future research and potential areas that require more attention and improvement in this field are highlighted.https://ieeexplore.ieee.org/document/10734108/AndroidAndroid malwaremalware detectionsupervised machine learning |
| spellingShingle | Safa J. Altaha Ahmed Aljughaiman Sonia Gul A Survey on Android Malware Detection Techniques Using Supervised Machine Learning IEEE Access Android Android malware malware detection supervised machine learning |
| title | A Survey on Android Malware Detection Techniques Using Supervised Machine Learning |
| title_full | A Survey on Android Malware Detection Techniques Using Supervised Machine Learning |
| title_fullStr | A Survey on Android Malware Detection Techniques Using Supervised Machine Learning |
| title_full_unstemmed | A Survey on Android Malware Detection Techniques Using Supervised Machine Learning |
| title_short | A Survey on Android Malware Detection Techniques Using Supervised Machine Learning |
| title_sort | survey on android malware detection techniques using supervised machine learning |
| topic | Android Android malware malware detection supervised machine learning |
| url | https://ieeexplore.ieee.org/document/10734108/ |
| work_keys_str_mv | AT safajaltaha asurveyonandroidmalwaredetectiontechniquesusingsupervisedmachinelearning AT ahmedaljughaiman asurveyonandroidmalwaredetectiontechniquesusingsupervisedmachinelearning AT soniagul asurveyonandroidmalwaredetectiontechniquesusingsupervisedmachinelearning AT safajaltaha surveyonandroidmalwaredetectiontechniquesusingsupervisedmachinelearning AT ahmedaljughaiman surveyonandroidmalwaredetectiontechniquesusingsupervisedmachinelearning AT soniagul surveyonandroidmalwaredetectiontechniquesusingsupervisedmachinelearning |