Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial Networks
With the widespread adoption of smartphones over the past decade, mobile applications have become a primary target for malicious attacks, usually in the form of malware. Recent studies have leveraged artificial intelligence (AI) techniques for malware detection and classification. However, applying...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11062843/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850117127749500928 |
|---|---|
| author | Shamma Alshebli Hyeran Mun Deepak Puthal Mohamed Jamal Zemerly Luigi Martino Ernesto Damiani Chan Yeob Yeun |
| author_facet | Shamma Alshebli Hyeran Mun Deepak Puthal Mohamed Jamal Zemerly Luigi Martino Ernesto Damiani Chan Yeob Yeun |
| author_sort | Shamma Alshebli |
| collection | DOAJ |
| description | With the widespread adoption of smartphones over the past decade, mobile applications have become a primary target for malicious attacks, usually in the form of malware. Recent studies have leveraged artificial intelligence (AI) techniques for malware detection and classification. However, applying such approaches, particularly deep learning (DL) techniques, to mobile malware detection poses significant challenges. These challenges arise from the difficulty of collecting large quantities of mobile malware samples and the inherent class imbalance in the collected datasets. To tackle these issues and enhance the performance of machine learning (ML) and DL detection models, we propose novel detection models based on a generative adversarial network (GAN). Furthermore, our approach not only employs a conditional tabular GAN (CTGAN) for data augmentation to explore the impact of augmentation but also identifies the optimal multiplication factor for achieving the best results. The evaluation results demonstrate that the proposed data augmentation approach significantly improves the performance of mobile malware detection models, especially those based on DL. We have notably figured out that doubling the original dataset is sufficient to enhance the performance of ML models, whereas DL models require additional data to achieve optimal results. Hence, our proposed mechanism is an effective solution for improving mobile malware detection. |
| format | Article |
| id | doaj-art-c5ac83f5e441425fa5754ff562148d05 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c5ac83f5e441425fa5754ff562148d052025-08-20T02:36:09ZengIEEEIEEE Access2169-35362025-01-011311589811590810.1109/ACCESS.2025.358524111062843Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial NetworksShamma Alshebli0https://orcid.org/0009-0007-8589-2544Hyeran Mun1https://orcid.org/0000-0002-5238-2392Deepak Puthal2https://orcid.org/0000-0002-5441-8934Mohamed Jamal Zemerly3https://orcid.org/0000-0003-1845-5946Luigi Martino4Ernesto Damiani5https://orcid.org/0000-0002-9557-6496Chan Yeob Yeun6https://orcid.org/0000-0002-1398-952XDepartment of Computer Science, Center for Secure Cyber-Physical Systems, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, Center for Secure Cyber-Physical Systems, Khalifa University, Abu Dhabi, United Arab EmiratesIndian Institute of Management Bodh Gaya, Bodh Gaya, IndiaDepartment of Computer Science, Center for Secure Cyber-Physical Systems, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, Center for Secure Cyber-Physical Systems, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, Center for Secure Cyber-Physical Systems, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, Center for Secure Cyber-Physical Systems, Khalifa University, Abu Dhabi, United Arab EmiratesWith the widespread adoption of smartphones over the past decade, mobile applications have become a primary target for malicious attacks, usually in the form of malware. Recent studies have leveraged artificial intelligence (AI) techniques for malware detection and classification. However, applying such approaches, particularly deep learning (DL) techniques, to mobile malware detection poses significant challenges. These challenges arise from the difficulty of collecting large quantities of mobile malware samples and the inherent class imbalance in the collected datasets. To tackle these issues and enhance the performance of machine learning (ML) and DL detection models, we propose novel detection models based on a generative adversarial network (GAN). Furthermore, our approach not only employs a conditional tabular GAN (CTGAN) for data augmentation to explore the impact of augmentation but also identifies the optimal multiplication factor for achieving the best results. The evaluation results demonstrate that the proposed data augmentation approach significantly improves the performance of mobile malware detection models, especially those based on DL. We have notably figured out that doubling the original dataset is sufficient to enhance the performance of ML models, whereas DL models require additional data to achieve optimal results. Hence, our proposed mechanism is an effective solution for improving mobile malware detection.https://ieeexplore.ieee.org/document/11062843/Malware classificationexplainable artificial intelligence (XAI)generative adversarial networks (GAN)data augmentationmachine learning (ML)deep learning (DL) |
| spellingShingle | Shamma Alshebli Hyeran Mun Deepak Puthal Mohamed Jamal Zemerly Luigi Martino Ernesto Damiani Chan Yeob Yeun Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial Networks IEEE Access Malware classification explainable artificial intelligence (XAI) generative adversarial networks (GAN) data augmentation machine learning (ML) deep learning (DL) |
| title | Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial Networks |
| title_full | Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial Networks |
| title_fullStr | Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial Networks |
| title_full_unstemmed | Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial Networks |
| title_short | Enhanced Android Malware Detect Models Based on Explainable Generative Adversarial Networks |
| title_sort | enhanced android malware detect models based on explainable generative adversarial networks |
| topic | Malware classification explainable artificial intelligence (XAI) generative adversarial networks (GAN) data augmentation machine learning (ML) deep learning (DL) |
| url | https://ieeexplore.ieee.org/document/11062843/ |
| work_keys_str_mv | AT shammaalshebli enhancedandroidmalwaredetectmodelsbasedonexplainablegenerativeadversarialnetworks AT hyeranmun enhancedandroidmalwaredetectmodelsbasedonexplainablegenerativeadversarialnetworks AT deepakputhal enhancedandroidmalwaredetectmodelsbasedonexplainablegenerativeadversarialnetworks AT mohamedjamalzemerly enhancedandroidmalwaredetectmodelsbasedonexplainablegenerativeadversarialnetworks AT luigimartino enhancedandroidmalwaredetectmodelsbasedonexplainablegenerativeadversarialnetworks AT ernestodamiani enhancedandroidmalwaredetectmodelsbasedonexplainablegenerativeadversarialnetworks AT chanyeobyeun enhancedandroidmalwaredetectmodelsbasedonexplainablegenerativeadversarialnetworks |