Dynamic management of a deep learning-based anomaly detection system for 5G networks

2018-05-05

Journal of Ambient Intelligence and Humanized Computing

AI Summary

This work proposes a deep learning–driven approach leveraging mobile edge computing (MEC) to address the challenges of real-time anomaly detection and efficient computational resource management in 5G networks, where massive connectivity and data traffic impose stringent demands. By deploying lightweight deep models at the network edge for real-time traffic analysis and integrating a policy-driven dynamic resource allocation mechanism, the proposed framework enables adaptive co-optimization between anomaly detection tasks and computational resources. Experimental results demonstrate that the solution significantly reduces detection latency and improves resource utilization efficiency in 5G environments, while maintaining high accuracy in anomaly identification.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• 5G networks
• anomaly detection
• mobile edge computing
• cybersecurity
• resource management

Innovation

• deep learning
• anomaly detection
• mobile edge computing
• dynamic resource management
• 5G networks