Ensemble Learning Method for DDOS Attack Mitigation in Web Based Networks
Keywords:
Ensemble Learning, DDoS Attack, Mitigation, Machine Learning, Random ForestAbstract
Web-based networks are threatened by DDoS attacks, which interrupt services and consume money. Ensemble learning methods are used to mitigate web-based DDoS attacks in this paper. Ensemble learning improves detection accuracy and robustness by combining machine learning models. The proposed algorithm detects DDoS attacks using Random Forest, an ensemble learning method. Random Forest may control high-dimensional data, analyse feature importance, and avoid overfitting. We gather and preprocess network traffic data, including packet speeds, sizes, and protocols. Labelled data classifies traffic occurrences as regular or DDoS for a Random Forest classifier. Accuracy, precision, recall, and F1-score are used to evaluate the model. Results show that ensemble learning can reliably predict DDoS attacks with few false positives. In web-based networks, this strong detection system monitors incoming traffic. Combining network security with ensemble learning-based DDoS mitigation, the proposed technique provides proactive DDoS defence. It protects services and user experiences from DDoS attacks because to its responsiveness to changing attack patterns and scalability. To evaluate the proposed approach, we have utilized two modern datasets, namely CIC-DDoS 2019 and AWID (A Network Intrusion Dataset). These results provide more support for the proposed models and allow the research to proceed in as planned proposed.
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