Deep Learning Techniques for Ransomware Detection and Prevention

Kevin William Peoples

Abstract: Ransomware attacks are threatening to both corporate and personal data significantly. The computer users face privacy violation and verification issues, reputation damage, and financial losses posed by successful assaults. Hence, it is important to identify and prevent ransomware attacks smoothly and accurately. Previous studies have proposed various approaches to detect ransomware, with their pros and cons. The core objective of this study is discussing existing ransomware attacks and deep learning methods to prevent those assaults. Hence, this study covers types of ransomwares, assault timeline, and deep learning methods. It also offers comprehensive research on existing approaches to detect, prevent, recover from, and reduce ransomware attacks. This study is based on systematic analysis of previous studies conducted from 2000 to 2024 to provide up-to-date knowledge to readers about the most recent advancements in terms of ransomware detection and highlights advanced approaches to combat those attacks. To conclude, this study underscores possible research concerns and challenges in terms of ransomware detection and prevention.

Keywords: ransomware detection, deep learning methods, ransomware attacks, assault timeline, privacy violation

How to Cite?: Kevin William Peoples, "Deep Learning Techniques for Ransomware Detection and Prevention", Volume 14 Issue 11, November 2025, International Journal of Science and Research (IJSR), Pages: 9232-934, https://www.ijsr.net/getabstract.php?paperid=SR251110122231, DOI: https://dx.doi.org/10.21275/SR251110122231