提高网络物理系统安全性的进展:用于网络流量分类和与安全技术集成的实用深度学习解决方案
Shivani Gaba1, Ishan Budhiraja1, Vimal Kumar1
1School of Computer Science Engineering and Technology, Bennett University, Greater Noida U.P., India.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
本研究介绍了一种混合深度学习 (DL) 模型,用于在网络物理系统 (CPS) 中准确的网络流量分类 (NTC). 该模型通过提高攻击检测和网络弹性来增强网络物理系统安全性.
科学领域:
- 网络安全 网络安全
- 计算机科学 计算机科学
- 网络工程 网络工程
背景情况:
- 传统的网络分析与复杂的网络威胁作斗争.
- 网络流量分类 (NTC) 对网络安全至关重要,但复杂.
- 机器学习 (ML) 模型为有效的NTC提供了潜力.
研究的目的:
- 引入一种新的混合深度学习 (DL) 模型,用于在网络物理系统 (CPS) 中增强NTC.
- 在CPS环境中提高网络安全的准确性和稳定性.
- 为了利用DL,NTC和CPS的集成来进行先进的威胁检测.
主要方法:
- 在Python中开发和实施混合DL模型.
- 专注于CPS的特定领域内的NTC.
- 使用准确度,精度,回忆和F1分数来评估模型性能.
主要成果:
- 混合DL模型在CPS的NTC中表现出更高的准确性.
- 该模型在CPS驱动的网络安全应用中被证明是稳健的.
- 关键性能指标证实了该模型的有效性.
结论:
- 拟议的混合DL模型显著提高了CPS中的NTC准确性.
- 这项研究有助于在动态CPS环境中对网络流量分类的弹性.
- 这些发现支持在互联系统中推进网络安全.
相关概念视频
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