Opcode Memory Analysis: A Data-Centric Machine Learning Framework for Early Detection and Attribution of Ransomware

Abstract Ransomware has emerged as one of the most significant threats in the cybersecurity landscape, causing widespread disruption and financial loss across various sectors. To address the growing sophistication of ransomware attacks, a novel machine learning framework leveraging opcode memory analysis has been developed, enabling the early detection and accurate attribution of ransomware. Through the systematic examination of low-level operational instructions within system memory, the proposed model distinguishes itself from traditional approaches by providing a more intrinsic understanding of malware behavior, leading to enhanced detection accuracy and the ability to identify specific ransomware families. The model's architecture, which includes a dual-output mechanism for simultaneous detection and attribution, demonstrates significant scalability and applicability across diverse operational environments. Extensive experimental results indicate that this approach not only surpasses existing methods in terms of detection performance but also offers a robust solution for real-time ransomware threat mitigation. The findings demonstrate the potential of opcode analysis as a critical component in the development of next-generation cybersecurity defenses, contributing to more resilient and proactive protective measures against evolving ransomware threats.