RealTime Ransomware Mitigation via Adaptive Machine Learning and Multi Layered Monitoring

In this project titled "Real-Time Ransomware Mitigation via Adaptive Machine Learning and Multi-Layered Monitoring," a robust system is developed to detect and mitigate ransomware attacks in real-time using adaptive machine learning models and multi-layered monitoring mechanisms. The system leverages advanced machine learning algorithms such as Light GBM, Random Forest, and XGBoost to identify potential ransomware behaviors based on a set of critical network traffic features, including packet size, header length, inter-arrival time (IAT), and data flow magnitude. These features are fed into pre-trained models, which classify network activity as either benign or malicious.

The system also integrates multi-layered monitoring, where data from various system components, including network traffic and system behavior, are continuously monitored to detect anomalies associated with ransomware attacks. This proactive approach ensures early detection, minimizing the risk of damage. The application is equipped with a user authentication mechanism, allowing administrators to log in and view predictions, along with features for model selection, which helps in adaptive learning for continuous improvement.

This real-time detection and mitigation framework provides an efficient and dynamic defense against ransomware attacks, minimizing the time window for attacks and offering real-time threat analysis. The system is designed to be scalable and adaptable, providing a solution that can evolve with emerging ransomware threats.

Keywords:
Ransomware Mitigation, Adaptive Machine Learning, Multi-Layered Monitoring, Real-Time Detection, Extra Trees, Random Forest, XGBoost, Network Traffic Analysis, Anomaly Detection, Cybersecurity.