基于区块链技术的联盟学习入侵检测系统

Ahmed Almaghthawi, Ebrahim A. A. Ghaleb, Nur Arifin Akbar, Layla Asiri, Meaad Alrehaili, Askar Altalidi
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引用次数: 0

摘要

本研究介绍了基于区块链的联合学习(FL)入侵检测系统的实施。这种方法利用机器学习(ML)代替传统的基于签名的方法,使系统能够检测到新的攻击类型。FL 技术在确保敏感数据隐私的同时,还能利用分布在客户端设备上的大量数据。为实现这一目标,我们采用了联合平均法,并加入了一个自定义预处理阶段,以实现数据标准化。区块链技术与 FL 的结合使用创建了一个完全去中心化的开放式学习系统,能够克服新的安全挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Federated-Learning Intrusion Detection System Based Blockchain Technology
This study presents the implementation of a blockchain-based federated-learning (FL) intrusion detection system. This approach utilizes machine learning (ML) instead of traditional signature-based methods, enabling the system to detect new attack types. The FL technique ensures the privacy of sensitive data while still utilizing the large amounts of data distributed across client devices. To achieve this, we employed the federated averaging method and incorporated a custom preprocessing stage for data standardization. The use of blockchain technology in combination with FL created a fully decentralized and open learning system capable of overcoming new security challenges.
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