Machine Learning Approaches to Ransomware Detection: A Comprehensive Review
Shayma Jawad, Hanaa Mohsin Ahmed · International Journal of Safety and Security Engineering · 2024
Ransomware is a widespread and dangerous cyberattack that encrypts data on systems and demands payment for decryption.This research provides a comprehensive review of ransomware detection methods, emphasizing machine learning-driven approaches.It explores dynamic analysis techniques, assesses detection frameworks, and highlights tools like SentinelOne and SandBlast Anti-Ransomware.Studies conducted between 2018 and 2023 were examined to compile the latest findings.The review underscores the effectiveness of predictive methods, with one approach achieving 99.9% accuracy using a pre-encryption detection algorithm.This work provides a valuable resource for understanding ransomware threats and offers actionable insights for enhancing detection and mitigation strategies.