Using Bidirectional Federated Transfer Learning for Intrusion Detection in Heterogeneous Industrial Internet of Things
Pages 115 - 123
Abstract
In the past few years, the development of Industry 4.0 has driven the demand for automation equipment in factories, thereby requiring better real-time monitoring tools to control the factory environment and equipment. Industrial Internet of Things (IIoT), as an important component of Industry 4.0, utilizes the capabilities of smart machines and real-time analytics to collect relevant data through various sensors, monitors, and other monitoring technologies deployed on devices, in order to improve operational efficiency. However, as the boundary between Information Technology (IT) and Operational Technology (OT) is rapidly disappearing, OT environments now require dedicated solutions for OT and IoT security. Due to the differences in packet features and labels collected in each domain, this study focuses on proposing a joint transfer learning approach combined with an intrusion detection system, and simulates datasets collected from different domains using three OT-related public datasets: Edge-IIoTset, WUSTL-IIOT-2021 Dataset, and Electra dataset (Modbus). It discusses the issues related to different attack types and dataset features. In addition, we compare the effects of different deep learning models on intrusion detection systems. The experimental results demonstrate that the proposed approach of bidirectional federated transfer learning enables cooperative learning of heterogeneous data.
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Index Terms
- Using Bidirectional Federated Transfer Learning for Intrusion Detection in Heterogeneous Industrial Internet of Things
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Published In
September 2023
170 pages
ISBN:9798400708046
DOI:10.1145/3627345
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Association for Computing Machinery
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Publication History
Published: 04 December 2023
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CCIOT 2023
CCIOT 2023: 2023 8th International Conference on Cloud Computing and Internet of Things
September 22 - 24, 2023
Okinawa, Japan
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