Distributed Denial of Service Attack Detection using Machine Learning

Abstract: The rapid growth of 5G communication and Internet of Things (IoT) devices has significantly increased the risk of Distributed Denial of Service (DDoS) attacks in modern networks. These attacks attempt to interrupt network services by flooding systems with excessive traffic, thereby affecting availability, reliability, and performance. Traditional detection mechanisms are often unable to identify evolving attack patterns efficiently, especially in high- speed 5G environments.
This research presents a machine learning-based framework for detecting DDoS attacks in 5G-enabled IoT networks. The proposed system utilizes Artificial Neural Networks (ANN) with Bayesian Regularization and backpropagation techniques to classify malicious and normal traffic. The model performs preprocessing, feature selection, training, and validation using network traffic datasets. The proposed framework improves detection accuracy while reducing false- positive rates. Experimental analysis demonstrates that machine learning methods can effectively identify abnormal traffic behavior and support real-time network protection mechanisms.

Keywords: DDoS Attack, Machine Learning, Artificial Neural Network, 5G Networks, IoT Security, Bayesian Regularization.