Email this article Building robust malware detection through conditional Generative Adversarial Network-based data augmentation
- Authors: Baghirov E.1
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Affiliations:
- Institute of Information Technology
- Issue: Vol 15, No 4 (2024)
- Pages: 97-110
- Section: Articles
- URL: https://bakhtiniada.ru/2079-3316/article/view/299214
- DOI: https://doi.org/10.25209/2079-3316-2024-15-4-97-110
- ID: 299214
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Abstract
Malware detection is essential in cybersecurity, yet its accuracy is often compromised by class imbalance and limited labeled data. This study leverages conditional Generative Adversarial Networks (cGANs) to generate synthetic malware samples, addressing these challenges by augmenting the minority class.The cGAN model generates realistic malware samples conditioned on class labels, balancing the dataset without altering the benign class. Applied to the CICMalDroid2020 dataset, the augmented data is used to train a LightGBM model, leading to improved detection accuracy, particularly for underrepresented malware classes.The results demonstrate the efficacy of cGANs as a robust data augmentation tool, enhancing the performance and reliability of machine learning-based malware detection systems.
About the authors
Elshan Baghirov
Institute of Information Technology
Email: elsenbagirov1995@gmail.com
Elshan Baghirov is a PhD candidate at the Institute of Information Technology, Baku, Azerbaijan. His research focuses on machine learning, and cybersecurity, with a particular emphasis on malware detection. He is also a data scientist at Kapital Bank OJSC, where he applies advanced analytics and predictive modeling to real-world business problems. His scientific interests include artificial intelligence, deep learning, and cybersecurity.
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