在网络入侵检测系统中用于罕见攻击类检测的联合转移学习.
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
本研究引入了具有适应层和转移学习 (TL) 的联合学习 (FL) 框架,以增强网络入侵检测系统 (NIDS). 这种新的方法提高了罕见和零日网络攻击的识别能力,提高了网络安全防御能力.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 允许在减少数据共享的情况下进行模型培训,从而改善隐私.
- 有效的基于FL的网络入侵检测系统 (NIDS) 被需要广泛,多样化的数据集所阻碍.
- 检测罕见和零日网络攻击仍然是NIDS的一个重大挑战.
研究的目的:
- 引入一个新的FL框架,以提高NIDS的性能.
- 改进罕见攻击类的检测,并识别零日攻击.
- 为了减少新型攻击类型的错误报警率.
主要方法:
- 在FL框架内,在客户级别内纳入适应性,个性化层.
- 利用转移学习 (TL) 进行零日攻击识别.
- 使用特定于客户端的梯度来更新服务器端的全球模型.
主要成果:
- 拟议的FL框架在多个数据集中检测罕见和新型攻击类型方面表现出卓越的表现 (CICIDS-2018,Edge IIoT,UNSW-NB 15).
- 实现了高准确率:在CICIDS-2018上达到了98.90%,在UNSW-NB 15上达到了98.70%,在Edge-IIoT上达到了97.92%.
- 显著优于FL-TL-CNN模型,表明增强了强度和适应性.
结论:
- 新的FL框架有效地解决了NIDS的挑战,特别是在罕见和零日攻击方面.
- 适应性,个性化和TL增强的方法为入侵检测提供了强大而可持续的解决方案.
- 该研究强调了该框架在异质网络环境中的适应性,改善了整体的网络安全态度.
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