卷积神经网络和专家组合用于5G网络及其他网络的入侵检测
Loukas Ilias1, George Doukas1, Vangelis Lamprou1
1Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
Frontiers in artificial intelligence
|January 21, 2026
概括
这项研究引入了一种新的专家混合 (MoE) 模型,用于先进的6G/NextG网络入侵检测. 由人工智能驱动的方法显著提高了恶意流量识别,提高了网络安全.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 电信 电信服务 电信服务 电信服务
背景情况:
- 6G/NextG网络承诺提高容量和效率,但面临着新出现的安全威胁.
- 目前的入侵检测系统经常使用静态深度神经网络,限制了它们的有效性.
- 对于网络安全,需要更具动态性和效率的AI算法.
研究的目的:
- 提出和评估一种新的专家混合 (MoE) 架构,用于在6G/NextG环境中增强网络入侵检测.
- 解决静态深度神经网络在识别复杂恶意流量的局限性.
- 提高基于人工智能的入侵检测系统的代表性能力和效率.
主要方法:
- 网络流量数据从1D特征阵列转换为2D矩阵.
- 应用了一个卷积神经网络 (CNN) 层,其次是批量规范化和最大聚合.
- 一个密集的专家网络和一个路由器的稀疏封闭的专家混合 (MoE) 层被实施用于选择性专家利用.
主要成果:
- 提出的MoE模型在5G-NIDD数据集上达到99.96%的高准确率,在NANCY数据集上达到79.59%.
- 废弃实验证实了集成MoE架构的有效性.
- 与现有的最先进的入侵检测方法相比,该模型表现出更高的性能.
结论:
- 集成一个稀有门的MoE架构代表了6G/NextG网络入侵检测的重大进步.
- 拟议的模型在识别恶意网络流量方面提供了更高的准确性和效率.
- 这种由人工智能驱动的方法为减轻下一代网络中新出现的安全威胁提供了强大的解决方案.
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