Advanced Ransomware Detection Framework using Memory Forensics and Deep Learning

Abstract

Ransomware has emerged as one of the most pervasive and damaging cybersecurity threats, with attacks targeting individuals, corporations, and critical infrastructure. Traditional detection techniques such as signature-based and heuristic methods often fail to identify novel or obfuscated ransomware variants, especially those employing polymorphic and zero-day tactics. This research proposes an \textbf{Advanced Ransomware Detection Framework} that synergizes digital forensics with deep learning techniques to detect and classify ransomware effectively. The framework begins with the forensic acquisition of behavioral data, including system calls, registry modifications, file system changes, and entropy analysis. These artifacts are then processed to extract static and dynamic features that capture both execution patterns and contextual anomalies. The results demonstrate that the fusion of forensic intelligence and deep behavioral modeling offers a powerful and scalable solution for ransomware detection, promising real-world applicability in enterprise and cloud security environments.