Ransomware Detection with Opcode Analysis and GAN-Based Unsupervised Learning

Abstract

This study introduces an innovative approach to ransomware detection utilizing opcode analysis combined with Generative Adversarial Networks (GANs). Focusing on the dynamic nature of modern ransomware threats, the research develops a method that leverages unsupervised learning to detect both known and novel ransomware variants. The study begins by examining the evolution of ransomware, from its initial focus on Windows-based systems to the current sophisticated attacks on various platforms. It then explores the implementation of a GAN-based model, capable of discerning ransomware through complex opcode patterns. Experimental results demonstrate the model's effectiveness across several ransomware families, with high accuracy, precision, recall, and F1-scores. The research further delves into the implications of advanced ransomware detection techniques, challenges in adapting to evolving ransomware strategies, the integration of AI in cybersecurity, and future directions in ransomware mitigation. This paper contributes significantly to the field of cybersecurity by providing an advanced, adaptable, and efficient tool for ransomware detection, marking a step forward in combating the increasing ransomware threat.