Performance optimization in ddos prediction with ensemble based approach

Distributed Denial of Service (DDoS) attacks pose a significant threat to network infrastructures, leading to service disruptions and potential financial losses. In this study, we propose an ensemble-based approach for DDoS attack detection, leveraging the strengths of three different classifiers: Adaboost, K-Nearest Neighbors (KNN), and Random Forest. We apply data normalization during pre-processing, utilize a Multi-Layer Perceptron (MLP) for feature extraction, and combine the classifiers using an ensemble approach. The performance of each individual classifier and the ensemble is extensively evaluated, and the results demonstrate the effectiveness of the proposed ensemble in accurately identifying and mitigating DDoS attacks.