Predictive Machine Learning Models for DDoS Attack Mitigation

This research provides a comprehensive method for enhanced Distributed Denial of Service (DDoS) detection and mitigation by utilizing ensemble learning, namely the Random Forest, Gradient Boosting, and AdaBoost algorithms. This may be done by combining these three strategies of learning. These algorithms were chosen for their demonstrated capacity to analyze high-dimensional data, understand complex connections, and adapt to new forms of attack. The dataset is then divided into training, validation, and testing sets to assess the model's resilience. Predictions obtained because of independent training of each basic technique on the training dataset were labeled RFi(X), GBi(X), and ABi(X), respectively. The weights for this approach are determined by a procedure known as cross-validation. The ensemble forecast is used to establish a threshold, and when a DDoS attack reaches that level, mitigation is activated. One method for dealing with the problem is traffic rerouting. Other methods include rate limiting, temporary IP address bans, and the adoption of new machine learning models for more in-depth research. The ensemble learning model's efficiency is reviewed on a regular basis, and changes are made as needed to account for developing trends in DDoS assaults. The suggested technique has several advantages and benefits over more standard DDoS protection systems. These enhancements include higher rates of true positives, lower rates of false alarms, and increased accuracy and precision. This illustrates its agility in both responding to and defending against a wide range of DDoS attack routes.