Detection of unseen malware threats using generative adversarial networks and deep learning models

The fast advancement of malware makes it an urgent problem for cybersecurity, as perpetrators consistently devise obfuscation methods to avoid detection. Conventional malware detection methods falter against polymorphic and zero-day threats, requiring more resilient and adaptable strategies. This article presents a Generative Adversarial Network (GAN)-based augmentation framework for malware detection, utilizing Convolutional Neural Networks (CNNs) to categorize malware variants efficiently. Synthetic malware images were developed using the Malevis dataset through Vanilla GAN and 4-Vanilla GAN to augment the diversity of the training dataset and enhance detection efficacy. Experimental findings indicate that training convolutional neural networks on datasets enhanced by generative adversarial networks enhances classification accuracy, with the 4-Vanilla GAN method achieving the maximum performance. Essential evaluation criteria, such as accuracy, precision, recall, FID score, Inception Score, and Diversity Score, validate the effectiveness of GAN-based augmentation. This study highlights the capability of deep learning in enhancing malware detection against new threats. Using a simplified GAN model (Dummy Generator) to create realistic grayscale malware variants from binary executables is what makes this study innovative. Furthermore, a CNN-LSTM hybrid architecture is suggested in order to capture malware patterns' sequential and spatial properties. Even with a little amount of labelled data, this combination allows for efficient categorization. Our GAN-based strategy improves dataset variety in a malware-specific environment, in contrast to traditional augmentation techniques.