Department of Electronics Information and System Engineering, Sangmyung University, Cheonan 31066, Korea.
Department of Information Security Engineering, Sangmyung University, Cheonan 31066, Korea.
Sensors (Basel). 2022 May 18;22(10):3819. doi: 10.3390/s22103819.
The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine learning (ML) technology in 5G networks is increasing. ML-based DDoS attack detection in a 5G environment should provide ultra-low latency. To this end, utilizing a feature-selection process that reduces computational complexity and improves performance by identifying features important for learning in large datasets is possible. Existing ML-based DDoS detection technology mostly focuses on DDoS detection learning models on the wired Internet. In addition, studies on feature engineering related to 5G traffic are relatively insufficient. Therefore, this study performed feature selection experiments to reduce the time complexity of detecting and analyzing large-capacity DDoS attacks in real time based on ML in a 5G core network environment. The results of the experiment showed that the performance was maintained and improved when the feature selection process was used. In particular, as the size of the dataset increased, the difference in time complexity increased rapidly. The experiments show that the real-time detection of large-scale DDoS attacks in 5G core networks is possible using the feature selection process. This demonstrates the importance of the feature selection process for removing noisy features before training and detection. As this study conducted a feature study to detect network traffic passing through the 5G core with low latency using ML, it is expected to contribute to improving the performance of the 5G network DDoS attack automation detection technology using AI technology.
5G 网络旨在实现具有低延迟的大规模物联网 (IoT) 环境。安全性较弱的物联网设备可能会对 5G 移动网络发起 Tbps 级别的分布式拒绝服务 (DDoS) 攻击。因此，人们越来越感兴趣的是在 5G 网络中使用机器学习 (ML) 技术自动进行网络入侵检测。5G 环境中基于 ML 的 DDoS 攻击检测应提供超低延迟。为此，利用特征选择过程通过在大数据集中识别对学习重要的特征来降低计算复杂度并提高性能是可能的。现有的基于 ML 的 DDoS 检测技术主要集中在有线互联网上的 DDoS 检测学习模型上。此外，与 5G 流量相关的特征工程研究相对不足。因此，本研究在 5G 核心网络环境中基于 ML 进行了特征选择实验，以减少实时检测和分析大容量 DDoS 攻击的时间复杂度。实验结果表明，使用特征选择过程可以保持和提高性能。特别是随着数据集大小的增加，时间复杂度的差异迅速增加。实验表明，使用特征选择过程可以实时检测 5G 核心网络中的大规模 DDoS 攻击。这证明了特征选择过程在训练和检测之前去除噪声特征的重要性。由于本研究使用 ML 进行了一项特征研究，以实时检测通过具有低延迟的 5G 核心的网络流量，因此有望有助于提高使用人工智能技术的 5G 网络 DDoS 攻击自动化检测技术的性能。
