Federated Learning Approach to Classification of DDoS Attacks by 2D-Convolutional Neural Network

This paper presents a federated learning approach to the classification of Distributed Denial of Service (DDoS) attacks using convolutional neural networks. The study employs the CIC-DDoS2019 dataset, which includes a variety of benign and malicious network traffic data. The classification task focuses on identifying specific types of DDoS attacks, such as Domain Name System (DNS), PORTMAP, and SYN Flood, alongside non-DDoS traffic. Utilizing MATLAB’s parallel server architecture, the research implements federated learning to train local models on distributed datasets, enhancing data privacy and security. The federated learning model achieves a global accuracy of 0.894 after 58 communication rounds (174 epochs), compared to a standalone training accuracy of 0.9607 after 300 epochs. While standalone training benefits from centralized data leading to higher accuracy, federated learning offers significant advantages in terms of scalability, privacy preservation, and utilization of decentralized computational resources.