使用分布式机器学习框架对MQTT协议进行安全增强
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Updated: Jul 24, 2026
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Nouf Saeed Alotaibi1, Hassan I Sayed Ahmed2, Samah Osama M Kamel2
1Department of Computer Science, College of Science and Humanities Al Dawadmi, Shaqra University, Dawadmi City 11911, Saudi Arabia.
Sensors (Basel, Switzerland)
|March 13, 2024
概括
本研究介绍了一种基于H2O的机器学习框架,以提高物联网网络中的消息队列远程测量传输 (MQTT) 安全性. H2OXGBoost算法在实时检测和分类MQTT攻击方面表现出卓越的准确性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 消息队列遥测传输 (MQTT) 广泛用于设备数据传输,但缺乏固有的安全性,使其易受各种攻击,如DDoS和MitM.
- 现有的安全解决方案难以应对现代物联网环境的可扩展性和复杂性.
研究的目的:
- 提出和评估基于H2O的新型分布式机器学习框架,用于实时检测和分类MQTT安全威胁.
- 确定最有效的H2O算法,以提高物联网网络中的MQTT安全性.
主要方法:
- 使用H2O算法 (随机森林,GLM,GBM,XGBoost,DL) 的分布式机器学习框架的开发.
- 在MQTT通信中实时监控和分类异常行为.
- 使用MSE,RMSE,MCE和日志损失等指标进行绩效评估.
主要成果:
- 在检测MQTT攻击时,H2OXGBoost算法在评估的H2O模型中表现出最高的准确性.
- 拟议的框架允许有效的实时监控和安全威胁的分布式分类.
结论:
- 基于H2O的分布式ML框架显著提高了MQTT协议的安全性,特别是在物联网环境中.
- 这项研究提供了一个实际的解决方案,通过先进的检测技术来提高MQTT通信道的安全性.
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