Ransomware Prediction Using Machine Learning Project

Ransomware attacks have become a significant threat to computer systems and data, causing substantial financial losses and disruptions. This project proposes a machine learning-based approach to predict and detect ransomware attacks. The project collects and analyzes system logs, network traffic, and other relevant data to identify patterns and anomalies indicative of ransomware attacks. The project develops and trains machine learning models to predict ransomware attacks and evaluates their performance using metrics such as accuracy, precision, and recall. The project also implements a real-time detection system using the trained models. The results show that the proposed approach can effectively predict and detect ransomware attacks, providing a valuable tool for organizations to protect themselves against these threats.

Keywords: Ransomware, Machine Learning, Prediction, Detection, Cybersecurity, Malware, Artificial Intelligence, Deep Learning, Neural Networks, Data Analysis, Data Mining, Network Security, Computer Security.

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