Insight of Anomaly Detection with NWDAF in 5G
Yachao Yuan, Christian Gehrmann, Jakob Sternby, Luis Barriga
Abstract
Data analytics is regarded as an important function of 5G networks. The Network Data Analytics Function (NWDAF) is standardized in 3GPP to enhance 5G network performance by analyzing data from network functions and user equipment. Abnormal behavior detection, which is part of the NWDAF framework, has the potential to be a powerful tool to improve 5G network security. Despite this, only limited research has been conducted in the area so far. This paper explains abnormal behavior detection in NWDAF specified in 3GPP. Furthermore, we extensively review the related work and summarize open problems and provide possible future research directions.
Topics & Concepts
Computer scienceAnomaly detectionAnalyticsFunction (biology)Data scienceCellular networkData analysisOpen researchData miningComputer networkWorld Wide WebEvolutionary biologyBiologyNetwork Security and Intrusion DetectionAnomaly Detection Techniques and ApplicationsInternet Traffic Analysis and Secure E-voting