Analysis of Ransomware Attack Detection Using Machine Learning Algorithms

Abstract

Ransomware is one of the most prevalent and damaging forms of cyberattacks, causing substantial losses to organizations worldwide. The increasing sophistication of ransomware demands advanced detection techniques to identify and mitigate potential threats. This research explores machine learning models, including Random Forest, Gradient Boosting Machines (GBM), and Logistic Regression, to enhance ransomware attack detection. The study analyzes their performance using metrics such as precision, recall, and cross-validation accuracy. This paper aims to demonstrate the efficacy of machine learning in ransomware detection and provide a comparative analysis of these algorithms, identifying potential areas for future improvements. 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. In conclusion, this research highlights unanswered concerns and potential research challenges in ransomware detection.