Fog and mobile edge computing (MEC) will play a key role in the upcoming fifth generation (5G) mobile networks to support decentralized applications, data analytics and management into the network itself by using a highly distributed compute model. Furthermore, increasing attention is paid to providing user-centric cybersecurity solutions, which particularly require collecting, processing and analyzing significantly large amount of data traffic and huge number of network connections in 5G networks. In this regard, this paper proposes a MEC-oriented solution in 5G mobile networks to detect network anomalies in real-time and in autonomic way. Our proposal uses deep learning techniques to analyze network flows and to detect network anomalies. Moreover, it uses policies in order to provide an efficient and dynamic management system of the computing resources used in the anomaly detection process. The paper presents relevant aspects of the deployment of the proposal and experimental results to show its performance.

翻译：雾计算与移动边缘计算（MEC）将在即将到来的第五代（5G）移动网络中发挥关键作用，通过采用高度分布式的计算模型，将去中心化应用、数据分析与管理功能融入网络本身。此外，提供以用户为中心的网络安全解决方案日益受到重视，这尤其需要在5G网络中收集、处理和分析海量数据流量与海量网络连接。为此，本文提出一种面向5G移动网络的MEC解决方案，用于以自主方式实时检测网络异常。我们的方案采用深度学习技术分析网络流量并检测网络异常。此外，它利用策略机制为异常检测过程中使用的计算资源提供高效动态的管理系统。本文阐述了该方案部署的相关要点及展示其性能的实验结果。