Dynamic Management of a Deep Learning-Based Anomaly Detection System for 5G Networks

TL;DR

This paper presents a MEC-oriented deep learning system for real-time anomaly detection in 5G networks, with dynamic resource management to enhance performance and scalability.

Contribution

It introduces a novel autonomous system combining deep learning and policy-based resource management for 5G network anomaly detection.

Findings

Effective real-time anomaly detection demonstrated

Dynamic resource management improves system efficiency

Deployment results show promising performance

Abstract

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.