• Sheetu Bala
• Harjinder kaur

This research paper proposes an ensemble-based approach for the detection of Distributed Denial of Service (DDoS) attacks. DDoS attacks continue to pose a significant threat to network infrastructure and service availability. Traditional single-model detection methods often struggle to keep up with the evolving tactics employed by attackers. To address this challenge, we introduce an ensemble approach that combines the strengths of multiple detection models to enhance accuracy and robustness in identifying DDoS attacks. Our methodology involves aggregating the outputs of individual detectors using a suitable fusion technique. Through extensive experimentation and comparison with existing methods, we demonstrate the superiority of our ensemble-based approach in accurately identifying and mitigating DDoS attacks.

DDoS Attack Detection,Ensemble Approach,Cybersecurity,,Machine Learning,Network Traffic Analysis

"Enhancing DDoS Attack Detection: A Comparative Analysis of Ensemble-Based Approach with SVM and Random Forest", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.g363-g370, September-2023, Available :http://www.jetir.org/papers/JETIR2309656.pdf

"Enhancing DDoS Attack Detection: A Comparative Analysis of Ensemble-Based Approach with SVM and Random Forest", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppg363-g370, September-2023, Available at : http://www.jetir.org/papers/JETIR2309656.pdf