DFSat: Deep Federated Learning for Identifying Cyber Threats in IoT-based Satellite Networks

Abstract

The integration of satellite systems with smart computing and networking technologies, such as the Internet of Things (IoT), has intensely augmented sophisticated cyberattacks against satellite environments. Resisting cyber threats to complex and large-scale satellite configurations has been enormously challenging, owing to the deficiency of high-quality samples of attack data collected from distributed satellite networks. This study proposes a novel federated learning-based deep learning framework for intrusion detection, named DFSat, to identify cyberattacks from IoT-integrated satellite networks. We develop a distributed deep learning-enabled attack detection method using a recurrent neural network. We then build a federated learning architecture which, utilizes several IoT-integrated satellite networks to preserve the privacy and security of DFSat's parameters throughout the learning process. Extensive experiments have been conducted using communication rounds on an IoT-based network dataset to validate the efficiency of DFSat. The results revealed that the proposed framework significantly distinguishes complex cyberattacks, outperforming recent state-of-the-art intrusion detection techniques, validating its usefulness as a viable deployment framework in IoT-integrated satellite networks.