一个基于深度学习的混合入侵检测系统,用于物联网网络.
Noor Wali Khan1, Mohammed S Alshehri2, Muazzam A Khan1,3
1Department of Computer Science, Quaid-i-Azam University, Islamabad 44000, Pakistan.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
本研究介绍了使用深度学习的物联网 (IoT) 网络的智能入侵检测系统 (IDS). 新的RNN-GRU模型有效地检测所有物联网层的各种网络攻击,增强网络安全.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 深度学习应用程序
背景情况:
- 物联网 (IoT) 网络面临着重大安全挑战,现有的入侵检测系统 (IDS) 通常仅限于单层分析.
- 由于不断变化的威胁环境,对物联网环境中全面,多层次的入侵检测的需求至关重要.
研究的目的:
- 为物联网网络提出和评估智能IDS,能够在物理,网络和应用层检测入侵.
- 为了利用深度学习,特别是循环神经网络通道循环单元 (RNN-GRU),进行增强的多层攻击分类.
主要方法:
- 开发一种新的深度学习模型 (RNN-GRU) 以对物联网架构的三层攻击进行分类.
- 使用专门的ToN-IoT数据集训练和测试模型,其中包括新的攻击向量.
- 使用准确度,精度,回忆和F1分数等指标进行性能评估,并进行Adam优化.
主要成果:
- 拟议的RNN-GRU模型实现了高精度:99%的网络流数据集和98%的应用层数据集.
- 与各种先进的深度学习和传统的机器学习技术相比,表现出卓越的性能.
- 亚当优化器被证明是最佳的模型评估.
结论:
- 开发的智能IDS有效地解决了物联网中单层检测系统的局限性.
- RNN-GRU模型为物联网网络的多层入侵检测提供了强大而优异的解决方案.
- 这项研究在保护物联网方面取得了重大进展.
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