论文标题
多阶段的干扰攻击使用深度学习与5G云无线电访问网络中的内核支持向量机结合
Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks
论文作者
论文摘要
在5G网络中，云无线电访问网络（C-RAN）通过提供实时云基础架构，合作无线电和集中数据处理，被认为是有希望的未来体系结构。最近，鉴于他们容易出现恶意攻击，C-RAN网络的安全引起了极大的关注。在各种基于异常的侵入检测技术中，最有希望的是基于机器学习的入侵检测，因为它在没有人类援助的情况下学习并相应地调整了行动。在这个方向上，已经提出了许多解决方案，但是它们在攻击分类方面表现出较低的精度，或者仅提供一层攻击检测。这项研究重点是在5G C-RAN中部署基于多阶段的基于机器学习的入侵检测（ML-IDS），该检测可以检测和分类四种类型的干扰攻击：持续干扰，随机干扰，欺骗性障碍和反应性干扰。这种部署通过最大程度地减少C-RAN体系结构中的假否定性来增强安全性。使用WSN-DS（无线传感器网络数据集）对提出的解决方案进行实验评估，该数据集是一个专用的无线数据集，用于入侵检测。攻击的最终分类精度为94.51 \％，为7.84 \％假阴性率。
In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently by providing real-time cloud infrastructures, cooperative radio, and centralized data processing. Recently, given their vulnerability to malicious attacks, the security of C-RAN networks has attracted significant attention. Among various anomaly-based intrusion detection techniques, the most promising one is the machine learning-based intrusion detection as it learns without human assistance and adjusts actions accordingly. In this direction, many solutions have been proposed, but they show either low accuracy in terms of attack classification or they offer just a single layer of attack detection. This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN that can detect and classify four types of jamming attacks: constant jamming, random jamming, deceptive jamming, and reactive jamming. This deployment enhances security by minimizing the false negatives in C-RAN architectures. The experimental evaluation of the proposed solution is carried out using WSN-DS (Wireless Sensor Networks DataSet), which is a dedicated wireless dataset for intrusion detection. The final classification accuracy of attacks is 94.51\% with a 7.84\% false negative rate.
