Towards Decentralized IoT Security: Optimized Detection of Zero-Day Multi-Class Cyber-Attacks Using Deep Federated Learning

The exponential growth of the Internet of Things (IoT) has introduced significant security challenges, with zero-day attacks emerging as one of the most critical and challenging threats. Traditional Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated promising early detection capabilities. However, their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints, high computational costs, and the costly time-intensive process of data labeling. To address these challenges, this study proposes a Federated Learning (FL) framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks. By employing Deep Neural Networks (DNNs) and decentralized model training, the approach reduces computational complexity while improving detection accuracy. The proposed model demonstrates robust performance, achieving accuracies of 94.34%, 99.95%, and 87.94% on the publicly available kitsune, Bot-IoT, and UNSW-NB15 datasets, respectively. Furthermore, its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets, TON-IoT and IoT-23, using a Deep Federated Learning (DFL) framework, underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments. Experimental results demonstrate superior performance over existing methods, establishing the proposed framework as an efficient and scalable solution for IoT security.