用深度学习在5G边缘计算中实时自适应检测网络异常
Dynamic Management of a Deep Learning-Based Anomaly Detection System for 5G Networks
- 基于MEC架构，用深度学习分析网络流量
- 通过策略动态管理资源，实现实时异常检测
- 适合5G安全与边缘计算研究者参考
雾计算与移动边缘计算（MEC）将在下一代第五代（5G）移动网络中发挥关键作用，通过高度分布式的计算模型将应用、数据分析与管理下沉至网络边缘。随着用户为中心的网络安全方案日益重要，5G网络需处理海量数据流量与大量网络连接。本文提出一种面向MEC的5G网络异常检测方案，利用深度学习技术分析网络流并实现自主式实时异常检测。系统采用策略驱动机制，动态管理检测过程中的计算资源。论文展示了部署相关细节与实验结果，验证了方案的有效性。
原文摘要 · Abstract (English)
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.
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