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Abstract This study introduces an innovative approach to ransomware detection on Windows operating systems by leveraging Generative Adversarial Networks (GANs) to analyze file system I/O Request Packet (IRP) operations. The proposed method demonstrates a significant improvement in identifying ransomware activities through the dynamic monitoring of IRP operations, distinguishing between benign and malicious behaviors with high accuracy. The research highlights the application of GANs as a powerful tool in cybersecurity, capable of adapting to evolving ransomware tactics without the need for predefined threat signatures. Through rigorous testing, the model showcased notable advancements over traditional detection methods, indicating its potential to enhance real-world cybersecurity defenses. The findings suggest a shift towards more adaptive, machine learning-based solutions for combating the increasing complexity of cyber threats.
Xu et al. (Fri,) studied this question.