FL-DSFA:保护基于RPL的物联网网络免受使用联合学习的选择性转发攻击
Rabia Khan1, Noshina Tariq1, Muhammad Ashraf2
1Department of Avionics Engineering, Air University, Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
本研究介绍了FL-DSFA,一种联合学习技术,用于检测物联网 (IoT) 中的选择性转发攻击. 它通过高精度和高效率准确识别路由攻击来提高物联网安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 物联网 (IoT) 能够实现无设备集成,但面临着重大的安全挑战.
- 路由攻击,特别是对低功耗和丢失网络 (RPL) 路由协议的选择性转发攻击,对物联网系统构成严重威胁.
- 这些攻击针对控制消息和路由拓等关键组件,导致潜在的数据丢失和系统损坏.
研究的目的:
- 开发和评估一种新的基于学习的联合检测技术 (FL-DSFA),用于识别物联网网络中的选择性转发攻击.
- 加强物联网路由协议的安全性和可靠性,以防止复杂的攻击.
- 在资源有限的物联网环境中提高攻击检测机制的效率和隐私.
主要方法:
- 使用物联网路由攻击数据集 (IRAD) 实现轻量级联合学习模型 (FL-DSFA).
- 包括攻击类型,如Hello Flood (HF),降级 (DR) 和版本号 (VN) 进行全面检测.
- 对二进制分类算法的评估,包括后勤回归 (LR),K-最近邻居 (KNN),支持矢量机 (SVM) 和天真贝叶斯 (NB) 以提高训练效率.
主要成果:
- 在训练期间,FL-DSFA技术,特别是使用SVM和KNN分类器,显示出高精度和高效的运行时间性能.
- 拟议的系统实现了特殊的性能指标:97.50%的预测精度,95%的准确性,98.33%的回忆率,97.01%的F1得分.
- 对比分析证实了拟议方法在分类准确性,可扩展性和隐私方面比现有研究更优越.
结论:
- 联合学习提供了一个强大的解决方案,用于检测物联网网络中的选择性转发攻击.
- FL-DSFA技术提供了一个可扩展,高效和保护隐私的方法来加强物联网安全.
- 该研究强调了机器学习在保护关键物联网基础设施的潜力,以应对不断变化的网络威胁.
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