BFLIDS:用于IoMT网络入侵检测的区块链驱动的联合学习
Khadija Begum1, Md Ariful Islam Mozumder1, Moon-Il Joo1
1Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了BFLIDS,这是一个使用区块链和联合学习的新系统,用于在医疗物联网网络中安全检测入侵. 它在不集中敏感信息的情况下增强网络安全和数据隐私.
科学领域:
- 网络安全 网络安全
- 医疗保健技术 技术 医疗保健 技术
- 机器学习 机器学习
背景情况:
- 医疗物联网 (IoMT) 存在重大安全漏洞.
- 传统的安全措施对于动态的IoMT环境是不够的.
- 集中式机器学习入侵检测系统 (IDS) 由于单点故障,引发了隐私问题.
研究的目的:
- 为IoMT网络开发一个安全和保护隐私的IDS.
- 通过使用一种新的框架来增强入侵检测能力.
- 解决IOMT安全中集中式机器学习的局限性.
主要方法:
- 引入基于区块链授权的基于联邦学习的IDS (BFLIDS).
- 集成区块链来实现交易安全,联合学习来实现数据隐私,IPFS用于去中心化存储,MongoDB用于数据管理.
- 用Kullback-Leibler分歧和自适应加权对FedAvg算法的修改.
- 实现基于Adaptive Max Pooling的CNN和修改后的BiLSTM,注意分类.
主要成果:
- 实现了高精度:97.43% (CNNs/Edge-IIoTSet),96.02% (BiLSTM/Edge-IIoTSet),98.21% (CNNs/TON-IoT) 和97.42% (BiLSTM/TON-IoT) 在联合学习场景中.
- 与集中式方法相比,已经证明了竞争性表现.
- 验证了BFLIDS在检测IoMT网络入侵方面的有效性.
结论:
- BFLIDS有效地提高了IoMT网络中的安全性和隐私性.
- 拟议的系统提供了一个强大的解决方案,用于在资源有限和敏感的环境中进行入侵检测.
- 区块链和联合学习集成为IoMT网络安全提供了可扩展和安全的方法.
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