针对轻量级物联网入侵检测系统的跨层联合学习
Suzan Hajj1, Joseph Azar2, Jacques Bou Abdo3
1Imagerie et Vision Artificielle (ImVIA) Laboratory, Université de Bourgogne Franche-Comté, 21078 Dijon, France.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种用于物联网安全的联合入侵检测系统 (IDS). 轻量级系统通过协作抽样和异常检测将真实阳性率提高10%,保护数据隐私.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网 (IoT) 设备的快速扩张带来了重大的安全和隐私挑战.
- 保护物联网网络及其数据需要高效和保护隐私的解决方案,特别是对于资源有限的设备.
研究的目的:
- 为物联网网络提出一个联合采样和轻量级入侵检测系统 (IDS).
- 为了提高物联网安全性和数据隐私,使用半监督的,基于K的方法来检测异常.
主要方法:
- 在物联网设备上进行本地数据处理的联合学习框架.
- K-表示用于网络流量采样和异常识别的集群.
- 仅共享总结统计数据以维护数据隐私.
主要成果:
- 拟议的联合IDS有效地检测到物联网网络中的入侵.
- 该系统展示了适合资源有限的物联网设备的效率.
- 工人和中央协调员之间的合作可以将真正阳性率提高到10%.
结论:
- 联合IDS为物联网安全和隐私提供了可行的解决方案.
- 该系统平衡了检测性能 (精确召回权衡) 与隐私保护.
- 联合学习中的协作方法可以显著提高物联网环境中的入侵检测准确性.
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