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FD-IDS:联合学习与知识蒸用于非IID物联网环境中的入侵检测
Haonan Peng1, Chunming Wu1, Yanfeng Xiao2
1College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了FD-IDS,这是一种用于物联网 (IoT) 网络的新型入侵检测系统. 它通过联合学习和知识蒸来增强安全性和隐私,解决分布式,异质数据的挑战.
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:
- 传统的集中式学习与敏感的,非IID物联网数据作斗争.
- 确保数据隐私和处理分布式异质性是物联网安全的关键挑战.
- 现有的入侵检测系统 (IDS) 面临着物联网数据独特特征的局限性.
研究的目的:
- 提出一个新的IDS框架,FD-IDS,利用联合学习和知识蒸.
- 解决数据隐私问题以及物联网环境中非IID数据的复杂性.
- 提高物联网网络入侵检测的效率和有效性.
主要方法:
- 开发了FD-IDS框架,整合了联合学习和知识蒸 (KD).
- 员工相互信息,以有效选择功能.
- 将近位项与KD结合起来,以减轻非IID场景中的模型漂移.
主要成果:
- FD-IDS在Edge-IIoT和N-BaIoT数据集上展示了有希望的检测性能.
- 该框架有效地解决了数据隐私和分布式异质性问题.
- 实验结果验证了该系统减轻模型漂移的能力.
结论:
- 在物联网中,FD-IDS为安全和高效的入侵检测提供了一个强大的解决方案.
- 联合学习和KD的整合在处理非IID物联网数据方面被证明是有效的.
- 提出的方法提高了复杂网络环境中的培训效率和检测准确性.
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