A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms
Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection b...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8629067/ |
| _version_ | 1818911765554003968 |
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| author | Zhuo Ma Haoran Ge Yang Liu Meng Zhao Jianfeng Ma |
| author_facet | Zhuo Ma Haoran Ge Yang Liu Meng Zhao Jianfeng Ma |
| author_sort | Zhuo Ma |
| collection | DOAJ |
| description | Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm. First, we construct the control flow graph of the application to obtain API information. Based on the API information, we innovatively construct Boolean, frequency, and time-series data sets. Based on these three data sets, three detection models for Android malware detection regarding API calls, API frequency, and API sequence aspects are constructed. Ultimately, an ensemble model is constructed for conformity. We tested and compared the accuracy and stability of our detection models through a large number of experiments. The experiments were conducted on 10010 benign applications and 10683 malicious applications. The results show that our detection model achieves 98.98% detection precision and has high accuracy and stability. All of the results are consistent with the theoretical analysis in this paper. |
| first_indexed | 2024-12-19T23:03:54Z |
| format | Article |
| id | doaj.art-2fc540f5383d409eb63c231db14e35a4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T23:03:54Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2fc540f5383d409eb63c231db14e35a42022-12-21T20:02:26ZengIEEEIEEE Access2169-35362019-01-017212352124510.1109/ACCESS.2019.28960038629067A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning AlgorithmsZhuo Ma0https://orcid.org/0000-0001-6023-2864Haoran Ge1Yang Liu2Meng Zhao3Jianfeng Ma4School of Cyber Engineering, Xidian University, Xi’an, ChinaSchool of Cyber Engineering, Xidian University, Xi’an, ChinaSchool of Cyber Engineering, Xidian University, Xi’an, ChinaSchool of Cyber Engineering, Xidian University, Xi’an, ChinaSchool of Cyber Engineering, Xidian University, Xi’an, ChinaAndroid malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm. First, we construct the control flow graph of the application to obtain API information. Based on the API information, we innovatively construct Boolean, frequency, and time-series data sets. Based on these three data sets, three detection models for Android malware detection regarding API calls, API frequency, and API sequence aspects are constructed. Ultimately, an ensemble model is constructed for conformity. We tested and compared the accuracy and stability of our detection models through a large number of experiments. The experiments were conducted on 10010 benign applications and 10683 malicious applications. The results show that our detection model achieves 98.98% detection precision and has high accuracy and stability. All of the results are consistent with the theoretical analysis in this paper.https://ieeexplore.ieee.org/document/8629067/Control flow graphapplication programming interfacemachine learningmalware detection |
| spellingShingle | Zhuo Ma Haoran Ge Yang Liu Meng Zhao Jianfeng Ma A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms IEEE Access Control flow graph application programming interface machine learning malware detection |
| title | A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms |
| title_full | A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms |
| title_fullStr | A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms |
| title_full_unstemmed | A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms |
| title_short | A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms |
| title_sort | combination method for android malware detection based on control flow graphs and machine learning algorithms |
| topic | Control flow graph application programming interface machine learning malware detection |
| url | https://ieeexplore.ieee.org/document/8629067/ |
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