Enhancing Cybersecurity: Comparative Insights in Machine Learning Models for Ransomware Detection

Ransomware is a new cybersecurity attack with huge financial and operational impact in industries globally. In this paper, an investigation of utilizing machine learning algorithms for ransomware detection is performed and compared with conventional methods, which consistently fall prey to dynamically altering attacks. Various algorithms, such as Support Vector Machines, Random Forest, Gradient Boosting, Artificial Neural Networks, Logistic Regression and ensemble methods, have been evaluated, with ensemble method of Gradient Boosting and Logistic Regression proving validation accuracy of 100% and Random Forest showing validation accuracy of 100% and 99.99% Recall. These findings validate the viability of utilizing machine learning for both known and unknown forms of ransomware detection, current work opens avenues for developing sophisticated, adaptive anti-ransomware frameworks.