Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-Based IIoT Networks

【Author】 Yazdinejad, Abbas; Dehghantanha, Ali; Parizi, Reza M.; Hammoudeh, Mohammad; Karimipour, Hadis; Srivastava, Gautam

【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

【影响因子】11.648

【Abstract】Nowadays, blockchain-based technologies are being developed in various industries to improve data security. In the context of the Industrial Internet of Things (IIoT), a chain-based network is one of the most notable applications of blockchain technology. IIoT devices have become increasingly prevalent in our digital world, especially in support of developing smart factories. Although blockchain is a powerful tool, it is vulnerable to cyberattacks. Detecting anomalies in blockchain-based IIoT networks in smart factories is crucial in protecting networks and systems from unexpected attacks. In this article, we use federated learning to build a threat hunting framework called block hunter to automatically hunt for attacks in blockchain-based IIoT networks. Block hunter utilizes a cluster-based architecture for anomaly detection combined with several machine learning models in a federated environment. To the best of our knowledge, block hunter is the first federated threat hunting model in IIoT networks that identifies anomalous behavior while preserving privacy. Our results prove the efficiency of the block hunter in detecting anomalous activities with high accuracy and minimum required bandwidth.

【Keywords】Industrial Internet of Things; Blockchains; Smart manufacturing; Anomaly detection; Data models; Informatics; Training; Anomaly detection; blockchain; federated learning (FL); industrial Internet of Things (IIoT); Internet of Thing (IoT); threat hunting

【发表时间】2022 NOV

【收录时间】2022-10-17

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-物联网