Ransomware Detection Using Machine Learning: A Survey

Abstract

Ransomware attacks pose significant security threats to personal and corporate data and information. The owners of computer-based resources suffer from verification and privacy violations, monetary losses, and reputational damage due to successful ransomware assaults. As a result, it is critical to accurately and swiftly identify ransomware. Numerous methods have been proposed for identifying ransomware, each with its own advantages and disadvantages. The main objective of this research is to discuss current trends in and potential future debates on automated ransomware detection. This document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides comprehensive research on existing methods for identifying, avoiding, minimizing, and recovering from ransomware attacks. An analysis of studies between 2017 and 2022 is another advantage of this research. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection and highlights advancements in methods for combating ransomware attacks. In conclusion, this research highlights unanswered concerns and potential research challenges in ransomware detection.