Sybil attack defense in blockchain-based industrial IoT systems using decentralized federated learning

Abstract

Integrating blockchain into the Industrial Internet of Things (IIoT) has emerged as a promising solution for preserving data privacy and ensuring IoT security. Among various blockchain platforms, Ethereum stands out due to its support for smart contracts and its interoperability with lightweight communication protocols. Despite these advantages, particularly within Ethereum-based networks, IIoT systems remain vulnerable to large-scale threats such as Sybil attacks. These attacks pose a critical security risk because an adversary generates numerous fake entities to infiltrate and compromise the network, ultimately undermining its integrity and availability. Existing approaches utilize Ethereum smart contracts and lightweight protocols such as MQTT to secure IIoT communications, but often overlook sophisticated threats such as Sybil attacks, which introduce fraudulent nodes into the network. Conventional detection methods typically depend on centralized monitoring, undermining scalability and privacy, and there remains a lack of publicly available datasets representing adversarial behaviors in IIoT environments. In this paper, an Ethereum-based IIoT network is first developed, and a publicly available dataset is released through the GitHub repository. An advanced method is then proposed to detect and prevent Sybil attacks in a PoA-based IIoT network using decentralized federated learning. During the detection phase, a convolutional neural network (CNN) is employed within the decentralized federated learning framework, achieving an average detection accuracy and recall of 91.13% and 91.37% among clients, respectively. In the prevention phase, a secure smart contract is designed to manage a dynamic reputation system, effectively preventing Sybil nodes from remaining active on the network.