A Systematic Literature Review of Android Malware Detection Using Static Analysis
Android malware has been in an increasing trend in recent years due to the pervasiveness of Android operating system. Android malware is installed and run on the smartphones without explicitly prompting the users or without the user's permission, and it poses great threats to users such as the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9118907/ |
| _version_ | 1818323819657428992 |
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| author | Ya Pan Xiuting Ge Chunrong Fang Yong Fan |
| author_facet | Ya Pan Xiuting Ge Chunrong Fang Yong Fan |
| author_sort | Ya Pan |
| collection | DOAJ |
| description | Android malware has been in an increasing trend in recent years due to the pervasiveness of Android operating system. Android malware is installed and run on the smartphones without explicitly prompting the users or without the user's permission, and it poses great threats to users such as the leakage of personal information and advanced fraud. To address these threats, various techniques are proposed by researchers and practitioners. Static analysis is one of these techniques, which is widely applied to Android malware detection and can detect malware quickly and prohibit malware before installation. To provide a clarified overview of the latest work in Android malware detection using static analysis, we perform a systematic literature review by identifying 98 studies from January 2014 to March 2020. Based on the features of applications, we first divide static analysis in Android malware detection into four categories, which include Android characteristic-based method, opcode-based method, program graph-based method, and symbolic execution-based method. Then we assess the malware detection capability of static analysis, and we compare the performance of different models in Android malware detection by analyzing the results of empirical evidence. Finally, it is concluded that static analysis is effective to detect Android malware. Moreover, there is a preliminary result that neural network model outperforms the non-neural network model in Android malware detection. However, static analysis still faces many challenges. Thus, it is necessary to derive some novel techniques for improving Android malware detection based on the current research community. Moreover, it is essential to establish a unified platform that is used to evaluate the performance of a series of techniques in Android malware detection fairly. |
| first_indexed | 2024-12-13T11:18:45Z |
| format | Article |
| id | doaj.art-7d18fd37ba494584ad25676dc17c767b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:18:45Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7d18fd37ba494584ad25676dc17c767b2022-12-21T23:48:33ZengIEEEIEEE Access2169-35362020-01-01811636311637910.1109/ACCESS.2020.30028429118907A Systematic Literature Review of Android Malware Detection Using Static AnalysisYa Pan0Xiuting Ge1https://orcid.org/0000-0002-0289-7291Chunrong Fang2https://orcid.org/0000-0002-9930-7111Yong Fan3Department of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, ChinaDepartment of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, ChinaState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, ChinaDepartment of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, ChinaAndroid malware has been in an increasing trend in recent years due to the pervasiveness of Android operating system. Android malware is installed and run on the smartphones without explicitly prompting the users or without the user's permission, and it poses great threats to users such as the leakage of personal information and advanced fraud. To address these threats, various techniques are proposed by researchers and practitioners. Static analysis is one of these techniques, which is widely applied to Android malware detection and can detect malware quickly and prohibit malware before installation. To provide a clarified overview of the latest work in Android malware detection using static analysis, we perform a systematic literature review by identifying 98 studies from January 2014 to March 2020. Based on the features of applications, we first divide static analysis in Android malware detection into four categories, which include Android characteristic-based method, opcode-based method, program graph-based method, and symbolic execution-based method. Then we assess the malware detection capability of static analysis, and we compare the performance of different models in Android malware detection by analyzing the results of empirical evidence. Finally, it is concluded that static analysis is effective to detect Android malware. Moreover, there is a preliminary result that neural network model outperforms the non-neural network model in Android malware detection. However, static analysis still faces many challenges. Thus, it is necessary to derive some novel techniques for improving Android malware detection based on the current research community. Moreover, it is essential to establish a unified platform that is used to evaluate the performance of a series of techniques in Android malware detection fairly.https://ieeexplore.ieee.org/document/9118907/Android malware detectionstatic analysissystematic literature review |
| spellingShingle | Ya Pan Xiuting Ge Chunrong Fang Yong Fan A Systematic Literature Review of Android Malware Detection Using Static Analysis IEEE Access Android malware detection static analysis systematic literature review |
| title | A Systematic Literature Review of Android Malware Detection Using Static Analysis |
| title_full | A Systematic Literature Review of Android Malware Detection Using Static Analysis |
| title_fullStr | A Systematic Literature Review of Android Malware Detection Using Static Analysis |
| title_full_unstemmed | A Systematic Literature Review of Android Malware Detection Using Static Analysis |
| title_short | A Systematic Literature Review of Android Malware Detection Using Static Analysis |
| title_sort | systematic literature review of android malware detection using static analysis |
| topic | Android malware detection static analysis systematic literature review |
| url | https://ieeexplore.ieee.org/document/9118907/ |
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