SA-FLIDS:安全和认证的基于学习的智能网络入侵检测系统,用于智能医疗保健
Radjaa Bensaid1, Nabila Labraoui2, Ado Adamou Abba Ari3
1STIC Lab, Abou Bekr Belkaid Tlemcen University, Tlemcen, Algeria.
PeerJ. Computer science
|February 3, 2025
概括
本研究介绍了一个安全的联合学习入侵检测系统 (FL-IDS),用于智能医疗保健物联网. 它增强了医疗应用中的数据隐私和防范网络攻击的安全性.
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
- 人工智能的人工智能
背景情况:
- 智能医疗系统利用人工智能,云和物联网,但面临着物联网设备的局限性,如计算和存储限制.
- 物联网设备中的漏洞威胁到敏感健康数据的完整性和整体医疗保健系统的弹性.
- 基于网络的入侵检测系统 (NIDS) 对于保护智能医疗网络和减轻医疗物联网 (IoMT) 应用中的风险至关重要.
研究的目的:
- 建议使用区块链 (SA-FLIDS) 创建一个基于安全和认证联合学习的NIDS框架.
- 增强数据隐私并降低雾-IoMT支持的智能医疗保健系统的通信成本.
- 解决分散式学习中的漏洞,包括Sybil和Model Poisoning攻击.
主要方法:
- 实施基于区块链的自主身份 (SSI) 模型,用于客户端身份验证和安全通信.
- 采用了Trimmed Mean方法来进行可靠的数据聚合,减轻异常或恶意输入的影响.
- 开发了一个基于安全和认证的联合学习的NIDS (SA-FLIDS) 框架.
主要成果:
- 在SA-FLIDS框架证明了对实物物联网流量数据集 (CICIoT2023,EdgeIIoTset) 的对抗性攻击的特殊稳定性.
- 拟议的系统有效地改善了数据隐私,并减少了通信开销.
- 成功解决了分散式学习系统的弱点,提高了安全性.
结论:
- 基于IoMT的医疗保健应用程序的SA-FLIDS框架提供了一个有前途的解决方案.
- 区块链和联合学习的整合显著提高了智能医疗保健网络的弹性.
- 这种方法在不断变化的数字健康格局中提供了对网络威胁的强有力的防御.
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