一种基于个性化联合超级网络的聚合方法,用于入侵检测系统
Chunduru Sri Abhijit1, Y Annie Jerusha1, S P Syed Ibrahim2
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, 600127, India.
Scientific reports
|September 30, 2025
概括
个性化联合超级网络通过使用轻量级嵌入矢量来提高物联网网络的入侵检测,以实现高效,可适应的学习. 这克服了传统方法的局限性,提高了动态环境中的隐私和性能.
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:
- 传统的网络入侵检测系统 (NIDS) 与物联网数据可扩展性和隐私问题作斗争.
- 联合学习 (FL) 提供保护隐私的分布式培训,但在模型个性化和通信开销方面面临挑战,特别是在非IID环境中.
- 现有的安全增强增加了计算成本,并且仍然可能暴露数据模式.
研究的目的:
- 在动态物联网环境中为网络入侵检测系统 (NIDS) 开发一种新的,保护隐私的,高效的联合学习策略.
- 解决传统FL的局限性,包括需要统一的模型架构和重量聚合期间的高通讯开销.
- 增强NIDS中对异质和非IID数据分布的个性化和适应性.
主要方法:
- 为入侵检测系统 (PerFedHypID) 提出基于个性化联合超级网络的聚合策略.
- 使用嵌入矢量,而不是重量聚合,以实现更轻的计算和个性化的学习.
- 利用个性化层和基于超级网络的聚合来提高效率和适应性.
- 在非IID异质环境下对CSE-CICIDS-2018和UNSW-NB-15数据集进行PerFedHypID评估.
主要成果:
- 与最先进的个性化联合学习算法相比,PerFedHypID表现出更高的性能.
- 拟议的方法实现了强大的性能,并在动态物联网环境中提高了适应性.
- 嵌入矢量利用证明在计算上更轻,并实现了增强的个性化.
结论:
- 在基于物联网的NIDS中,PerFedHypID有效地解决了可扩展性和隐私方面的挑战.
- 基于超级网络的聚合策略为传统的FL方法提供了更高效和更具适应性的替代方案.
- 这种方法为异质,动态的物联网生态系统中的入侵检测提供了强大的解决方案.
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