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支持NIDD的轻量级入侵检测,用于在5G及以后的有效DDoS缓解

Iqra Javid¹, Sibaram Khara¹, Jaroslav Frnda^2,3

¹Department of Electrical, Electronics and Communication Engineering, Sharda University, Greater Noida, Uttar Pradesh, 201305, India.

Scientific reports

|November 25, 2025

概括

一种新的轻量级入侵检测模型提高了5G网络的安全性,在非IP数据交付场景中检测尸网络和DDoS攻击等威胁时达到99.64%的准确性.

科学领域:

网络安全网络安全
电信工程电信工程电信工程
机器学习机器学习

背景情况:

5G技术承诺高速连接,但也带来了新的安全挑战,特别是非IP数据交付 (NIDD).
强大的异常检测对于保护物联网 (IoT) 和其他网络免受5G环境中的入侵至关重要.
越来越依赖互联技术,需要智能和高效的方法来确保网络可用性,保密性和完整性.

研究的目的:

为5G和超越网络提出一种新的,轻量级的入侵检测模型.
在5G NIDD场景中应对诸如尸网络透和分布式拒绝服务 (DDoS) 攻击等安全威胁.
用5GNIDD数据集和各种机器学习分类器来评估模型的有效性.

主要方法:

开发了一个预处理模型,将Gini Importance用于特征选择.
使用了最先进的分类器:AdaBoost,Easy Ensemble,GRU,1D-CNN,LSTM和混合CNN-LSTM.
使用5GNIDD数据集进行实验,包括分析特征选择对精度的影响的案例研究.

主要成果:

拟议的模型实现了高精度,达到1D-CNN和混合CNN-LSTM分类器的99.64%.
获得了0.9830的精度,证明了该模型在识别网络异常方面的有效性.
实验突出了维度的诅咒对检测精度的影响,当使用一组减少的功能时.

结论:

新的轻量级入侵检测模型有效地提高了5G网络的安全性,特别是在NIDD场景中.
该模型在检测各种网络入侵方面表现出卓越的性能,确保数据完整性和网络可用性.
这些发现强调了先进的异常检测技术对未来无线通信系统的重要性.

关键词:

1D CNN 在线播放 5GNIDDDD 这是什么意思?简单的组合组合.在这里,GRU GRU GRU 这是LSTM的LSTM.在LSTM-CNN中使用.

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