Open Access
A Survey on Ransomware Detection and Prevention Using Machine Learning Models
4
Assistant Professor, Computer Engineering, Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar, India
Abstract
Ransomware is one of the most serious cyber-security threats to both individuals and organizations, causing fairly serious financial losses and operational disruption. Ransomware attacks are getting increasingly sophisticated, making it more difficult for traditional signature-based security mechanisms to work effectively, which is why intelligent detection and prevention are needed. The predominant approach used for the detection of ransomware is via pattern recognition and behavior analysis, which is the domain of machine learning. This work provides an in-depth analysis of ransomware, its variants, attack cycles, and the idea of ransomware-as-a-service (RaaS). Furthermore, it discusses both traditional and deep learning approaches to ransomware detection, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. Further, traditional and AI-based ransomware prevention methods, recovery procedures, and a comparative review of recent papers are discussed to identify the existing challenges and potential research directions in ransomware detection and prevention using machine learning.
Keywords
Ransomware,
Cybersecurity,
Machine Learning
References
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