Generative Adversarial Networks for Malware Detection in Cloud Computing Environments

Abstract

In recent years, the proliferation of sophisticated malware threats has necessitated the development of advanced detection techniques to safeguard computer systems and networks. Generative Adversarial Networks (GANs), a class of deep learning models comprising a generator and a discriminator trained in an adversarial manner, have emerged as a promising tool for improving malware detection capabilities. Traditional signature-based malware detection struggles to keep pace with the ever-evolving threat landscape in cloud computing environments. This paper proposes a novel approach utilizing Generative Adversarial Networks (GANs) for enhanced malware detection. The GAN architecture generates realistic malware samples, refining the discriminator's ability to differentiate between legitimate and malicious software. This fosters a model adept at identifying zero-day attacks and unseen malware variants. Evaluation in a cloud environment assesses detection accuracy, efficiency, and generalizability. Findings underscore GANs' potential to enhance cloud-based malware detection, securing the future of cloud computing.