Abstract
With in the dynamic realm of cyber threats, distributed denial of service (DDoS) attacks pose a serious threat. They can undermine network infrastructures and bring about service interruptions that cost money. Our research proposes an ensemble-based technique for DDoS attack detection in response to this problem. By combining the strengths of three distinct classifiers—Random Forest, K-Nearest Neighbors (KNN), and Adaboost—we create a powerful ensemble model. To ensure superior performance, we employ a Multi-Layer Perceptron (MLP) for intricate feature extraction and data normalization in the pre-processing stage. Together with individual classifiers, the ensemble's efficiency is carefully evaluated, verifying that it can accurately identify and counteract DDoS attacks. Motivated by the dynamic nature of DDoS attacks and their inability to be defended against by conventional defense mechanisms, our work is the first to apply machine learning to enhance detection. Ensemble approaches hold promise in addressing the evolving DDoS threat landscape because they combine multiple classifiers to enhance overall performance. The research adds a new dimension by combining MLP-based feature extraction with the Adaboost, KNN, and Random Forest classifiers to increase the discriminatory power of the model. Some of our objectives include building an ensemble-based DDoS attack detection system, evaluating individual classifier performance, comparing ensemble performance with individual classifiers, and using data normalization and MLP-based feature extraction. The research is methodically organized, with a literature review, methodology, performance analysis, ensemble approach analysis, and a concluding summary. The outcomes show the value of the recommended ensemble approach and pave the way for more advancements in DDoS attack detection methods, enhancing online service security and availability in the face of evolving cyber threats.