Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

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The industrial internet of things (IIoT) is an evolutionary extension of the traditional Internet of Things (IoT) into processes and machines for applications in the industrial sector. The IIoT systems generate a large amount of private and sensitive data i.e., stored and processed somewhere on the cloud-edge continuum. The IIoT devices, and the IIoT networks are subject to security mechanisms such as intelligent Intrusion Detection and Prevention Systems (IDS/IPS) systems, that can detect and respond unseen malicious network attacks. The adoption of centralized machine learning methods for IDS has become impractical due to the high computational cost and privacy concerns associated with storing large amounts of data on a single server along the cloud-edge continuum. The combination of federated learning and Blockchain has emerged as a promising advancement in addressing the challenge. Federated learning distributes learning to individual IIoT devices without compromising data privacy, while Blockchain enhances privacy and security. Many academic and industrial efforts outline IDS mechanisms using machine learning, deep learning, federated learning, and Blockchain technologies. The utilization of federated learning-based IDS has become increasingly popular and is now being applied to various tasks including IDS/IPS systems. However, existing intrusion detection systems (IDSs) survey are limited to the scope of classical machine learning and deep learning. To address this limitation, we analyze the IIoT literature that integrates Blockchain and federated learning to enhance IDSs and improve its threat detection capabilities. This survey explores the role of Blockchain and federated learning in addressing security and privacy issues, particularly those associated with IDS/IPS in IIoT networks. Insights on the possibilities of machine learning, federated learning, and Blockchain in supporting IDS to monitor IIoT network traffic for anomaly detection are discussed in detail through state of the art. Furthermore, we provide a set of recommendation based on our literature for the effective implementation of a Blockchain and federated learning-based network intrusion detection system. Finally, we summarize the study and highlight challenges as future research directions for Blockchain and Federated Learning-based technologies for cybersecurity and intrusion detection in IIoT.