Building robust malware detection through conditional Generative Adversarial Network-based data augmentation
Year: 2024
Type: article
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.
Author: Elshan Baghirov
Institution: Institute of Information Technology
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405259177