A Systematic Overview of Android Malware Detection
Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. To stay ahead of other similar review work attempting to deal with the serious security problem of the Android environment, this work not only summarizes the approaches in the malware...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.2007327 |
| _version_ | 1797641097854320640 |
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| author | Li Meijin Fang Zhiyang Wang Junfeng Cheng Luyu Zeng Qi Yang Tao Wu Yinwei Geng Jiaxuan |
| author_facet | Li Meijin Fang Zhiyang Wang Junfeng Cheng Luyu Zeng Qi Yang Tao Wu Yinwei Geng Jiaxuan |
| author_sort | Li Meijin |
| collection | DOAJ |
| description | Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. To stay ahead of other similar review work attempting to deal with the serious security problem of the Android environment, this work not only summarizes the approaches in the malware classification phase but also lays emphasis on the Android feature selection algorithm and presents some areas neglected in previous works in the field of Android malware detection, like limitations and commonly applied datasets in machine learning-based models. In this paper, the Android OS environment, feature selection, classification models, and confronted challenges of machine learning detection are described in detail. Based on the brief introduction to Android background knowledge, feature selection methods are elaborated from key perspectives as feature extraction, raw data preprocessing, valid feature subsets selection, and machine learning-based selection models. For the algorithms of the malware classification, machine learning methods are categorized according to different standards to present an all-around view. Furthermore, this paper focuses on the study of deterioration problems and evasion attacks in machine learning detectors. |
| first_indexed | 2024-03-11T13:40:38Z |
| format | Article |
| id | doaj.art-ae6688aa69b54dd6a461d8bb41945b9d |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:38Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-ae6688aa69b54dd6a461d8bb41945b9d2023-11-02T13:36:37ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2021.20073272007327A Systematic Overview of Android Malware DetectionLi Meijin0Fang Zhiyang1Wang Junfeng2Cheng Luyu3Zeng Qi4Yang Tao5Wu Yinwei6Geng Jiaxuan7Sichuan UniversitySichuan UniversitySichuan UniversitySichuan UniversitySichuan UniversitySichuan UniversitySichuan UniversitySichuan UniversityDue to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. To stay ahead of other similar review work attempting to deal with the serious security problem of the Android environment, this work not only summarizes the approaches in the malware classification phase but also lays emphasis on the Android feature selection algorithm and presents some areas neglected in previous works in the field of Android malware detection, like limitations and commonly applied datasets in machine learning-based models. In this paper, the Android OS environment, feature selection, classification models, and confronted challenges of machine learning detection are described in detail. Based on the brief introduction to Android background knowledge, feature selection methods are elaborated from key perspectives as feature extraction, raw data preprocessing, valid feature subsets selection, and machine learning-based selection models. For the algorithms of the malware classification, machine learning methods are categorized according to different standards to present an all-around view. Furthermore, this paper focuses on the study of deterioration problems and evasion attacks in machine learning detectors.http://dx.doi.org/10.1080/08839514.2021.2007327 |
| spellingShingle | Li Meijin Fang Zhiyang Wang Junfeng Cheng Luyu Zeng Qi Yang Tao Wu Yinwei Geng Jiaxuan A Systematic Overview of Android Malware Detection Applied Artificial Intelligence |
| title | A Systematic Overview of Android Malware Detection |
| title_full | A Systematic Overview of Android Malware Detection |
| title_fullStr | A Systematic Overview of Android Malware Detection |
| title_full_unstemmed | A Systematic Overview of Android Malware Detection |
| title_short | A Systematic Overview of Android Malware Detection |
| title_sort | systematic overview of android malware detection |
| url | http://dx.doi.org/10.1080/08839514.2021.2007327 |
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