Ransomware Detection and Classification Using Generative Adversarial Networks with Dynamic Weight Adaptation

Ransomware is a major digital threat on the cybersecurity landscape. Wa aim to introduce a novel GAN-based approach combined with dynamic weight adaptation, to significantly enhance ransomware detection and classification accuracy. The proposed method leverages the generative capabilities of GANs to produce synthetic ransomware samples that closely mimic real behavior, enabling more effective training and improved detection performance. The dynamic weight adaptation mechanism further enhances the model's ability to generalize and adapt to new and evolving ransomware threats, addressing the limitations of traditional static weight assignments. Experimental results demonstrate superior performance compared to traditional machine learning algorithms and contemporary deep learning models, with significant improvements in accuracy, precision, recall, and F1-score. The implications of these findings are profound for advancing cybersecurity defenses against ransomware attacks, providing a robust and reliable tool for practitioners in the field. The research highlights the potential for ongoing innovation and improvement in ransomware detection techniques, paving the way for more adaptive and resilient cybersecurity measures.