The development and implementation of Internet of Things (IoT) devices have been accelerated dramatically in recent years. As a result, a super-network is required to handle the massive volumes of data collected and transmitted to these devices. Fifth generation (5G) technology is a new, comprehensive wireless technology that has the potential to be the primary enabling technology for the IoT. The rapid spread of IoT devices can encounter many security limits and concerns. As a result, new and serious security and privacy risks have emerged. Attackers use IoT devices to launch massive attacks; one of the most famous is the Distributed Denial of Service (DDoS) attack. Deep Learning techniques have proven their effectiveness in detecting and mitigating DDoS attacks. In this paper, we applied two Deep Learning algorithms Convolutional Neural Network (CNN) and Feed Forward Neural Network (FNN) in dataset was specifically designed for IoT devices within 5G networks. We constructed the 5G network infrastructure using OMNeT++ with the INET and Simu5G frameworks. The dataset encompasses both normal network traffic and DDoS attacks. The Deep Learning algorithms, CNN and FNN, showed impressive accuracy levels, both reaching 99%. These results underscore the potential of Deep Learning to enhance the security of IoT devices within 5G networks.

翻译：近年来，物联网（IoT）设备的开发与部署进程显著加速。因此，需要构建超级网络来处理这些设备所收集与传输的海量数据。第五代（5G）技术作为一种新型综合性无线技术，有望成为物联网的核心使能技术。物联网设备的快速普及引发诸多安全限制与隐患，由此衍生出严峻的新型安全与隐私风险。攻击者利用物联网设备发动大规模攻击，其中最具代表性的是分布式拒绝服务（DDoS）攻击。深度学习技术已在检测与缓解DDoS攻击方面展现出有效性。本文针对5G网络环境下的物联网专用数据集，应用了卷积神经网络（CNN）和前馈神经网络（FNN）两种深度学习算法。我们使用基于OMNeT++框架的INET与Simu5G组件构建了5G网络基础设施。该数据集涵盖正常网络流量与DDoS攻击流量。CNN与FNN深度学习算法均展现出卓越的准确率，二者均达到99%。这些结果凸显了深度学习在增强5G网络内物联网设备安全性方面的巨大潜力。