DL-AMDet: Deep learning-based malware detector for android
The Android operating system, with its market share leadership and open-source nature in smartphones, has become the primary target of malware. However, detecting malicious Android processes has become a significant challenge because of the complexity of size, length, and associations of various imp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305323001436 |
| _version_ | 1797287595118428160 |
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| author | Ahmed R. Nasser Ahmed M. Hasan Amjad J. Humaidi |
| author_facet | Ahmed R. Nasser Ahmed M. Hasan Amjad J. Humaidi |
| author_sort | Ahmed R. Nasser |
| collection | DOAJ |
| description | The Android operating system, with its market share leadership and open-source nature in smartphones, has become the primary target of malware. However, detecting malicious Android processes has become a significant challenge because of the complexity of size, length, and associations of various important and distinctive elements of Android applications, such as API calls and system calls. In this paper DL-AMDet, a deep learning architecture is proposed to detect Android malware applications based on its static and dynamic features. DL-AMDet consists of two main detection models the first one uses CNN-BiLSTM deep learning method for detecting malware using static analysis. The other model utilizes deep Autoencoders as an anomaly detection model to identify the malware based on dynamic analysis. The performance of the DL-AMDet architecture is evaluated using two different datasets. The results show that DL-AMDet achieves a competitive malware detection accuracy of 99.935 % for static and dynamic analysis models combined. Additionally, the results emphasize the significance of CNN-BiLSTM and Deep Autoencoders models used in DL-AMDet to outperform the existing state-of-the-art techniques. |
| first_indexed | 2024-03-07T18:35:36Z |
| format | Article |
| id | doaj.art-c23a5241cc234ae4b235686eadbe2860 |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-03-07T18:35:36Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-c23a5241cc234ae4b235686eadbe28602024-03-02T04:55:18ZengElsevierIntelligent Systems with Applications2667-30532024-03-0121200318DL-AMDet: Deep learning-based malware detector for androidAhmed R. Nasser0Ahmed M. Hasan1Amjad J. Humaidi2Control and Systems Engineering Department, University of Technology-Iraq, Baghdad 10066, IraqControl and Systems Engineering Department, University of Technology-Iraq, Baghdad 10066, IraqCorresponding author.; Control and Systems Engineering Department, University of Technology-Iraq, Baghdad 10066, IraqThe Android operating system, with its market share leadership and open-source nature in smartphones, has become the primary target of malware. However, detecting malicious Android processes has become a significant challenge because of the complexity of size, length, and associations of various important and distinctive elements of Android applications, such as API calls and system calls. In this paper DL-AMDet, a deep learning architecture is proposed to detect Android malware applications based on its static and dynamic features. DL-AMDet consists of two main detection models the first one uses CNN-BiLSTM deep learning method for detecting malware using static analysis. The other model utilizes deep Autoencoders as an anomaly detection model to identify the malware based on dynamic analysis. The performance of the DL-AMDet architecture is evaluated using two different datasets. The results show that DL-AMDet achieves a competitive malware detection accuracy of 99.935 % for static and dynamic analysis models combined. Additionally, the results emphasize the significance of CNN-BiLSTM and Deep Autoencoders models used in DL-AMDet to outperform the existing state-of-the-art techniques.http://www.sciencedirect.com/science/article/pii/S2667305323001436Malware detectionAndroidDeep learningStatic analysisDynamic analysis |
| spellingShingle | Ahmed R. Nasser Ahmed M. Hasan Amjad J. Humaidi DL-AMDet: Deep learning-based malware detector for android Intelligent Systems with Applications Malware detection Android Deep learning Static analysis Dynamic analysis |
| title | DL-AMDet: Deep learning-based malware detector for android |
| title_full | DL-AMDet: Deep learning-based malware detector for android |
| title_fullStr | DL-AMDet: Deep learning-based malware detector for android |
| title_full_unstemmed | DL-AMDet: Deep learning-based malware detector for android |
| title_short | DL-AMDet: Deep learning-based malware detector for android |
| title_sort | dl amdet deep learning based malware detector for android |
| topic | Malware detection Android Deep learning Static analysis Dynamic analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2667305323001436 |
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