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

• URL: http://arxiv.org/abs/2204.09829v1
• Date: Thu, 21 Apr 2022 00:45:30 GMT
• Title: Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks
• Authors: Abbas Yazdinejad (Cyber Science Lab, School of Computer science, University of Guelph, ON, Canada), Ali Dehghantanha (Cyber Science Lab, School of Computer science, University of Guelph, ON, Canada), Reza M. Parizi (College of Computing and Software Engineering, Kennesaw State University, GA, USA), Mohammad Hammoudeh (Information & Computer Science Department, King Fahd University of Petroleum & Minerals, Saudi Arabia), Hadis Karimipour (School of Engineering, Department of Electrical and Software Engineering at the University of Calgary, Alberta, Canada) and Gautam Srivastava (Department of Math and Computer Science, Brandon University, Manitoba, Canada as well as with the Research Centre for Interneural Computing, China Medical University, Taichung)
• Abstract summary: We use Federated Learning (FL) to build a threat hunting framework called Block Hunter to automatically hunt for attacks in IIoT networks. Our results prove the efficiency of the Block Hunter in detecting anomalous activities with high accuracy and minimum required bandwidth.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 cyber attacks. Detecting anomalies in blockchain-based IIoT networks in smart factories is crucial in protecting networks and systems from unexpected attacks. In this paper, we use Federated Learning (FL) 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.

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