Blockchain-Based Federated Learning Using Machine Learning Classifier for IoT Intrusion Detection System
Year: 2024
Type: article
Abstract: As the number of users and applications in IoT networks increases, concerns about cyber attacks are growing. IoT systems and other industries have significantly increased the adoption of machine learning-based intrusion detection systems (IDS) to defend against cyber attacks. Due to computing costs and privacy issues, using traditional centralized machine learning (ML) approaches to analyze data stored on a single central server is not a reasonable solution. The most promising strategy to address this issue is federated learning (FL), which updates learning on edge devices without exposing personal data to a central server. Combining federated learning with blockchain (BC) can provide greater benefits in solving this problem due to enhanced levels of privacy and security. In this paper, we propose a blockchain-based architecture (BFLSID) to support federated learning and improve intrusion detection in IoT networks. We have also developed a distributed storage system that integrates InterPlanetary File System (IPFS) and Hyperledger Fabric to ensure the security of model training and detection results.
Authors: Seon-ja Lim, Eun-Hee Choi, Philjoo Choi, Suk-Hwan Lee, Ki-Ryong Kwon
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403500233