Securing the Internet of Robotic Things: A Federated Learning Approach
Matilda Nkoom, Daniel Commey, Sena G. Hounsinou, Garth V. Crosby · 2024
This paper addresses the challenge of Distributed Denial of Service (DDoS) attacks in the Internet of Robotic Things (IoRT) using a federated learning approach. We investigate the performance of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) for DDoS detection in IoRT systems. Our models are evaluated using the CICDDoS2019 dataset. The CNN-based model achieves the highest performance with an accuracy of 0.9810 and an F1-score of 0.9800, outperforming LSTM and GRU-based models. We analyze the models’ convergence properties and discuss their suitability for resource-constrained IoRT devices. Our results demonstrate the potential of federated learning for enhancing IoRT security while highlighting the trade-offs between model performance and efficiency.