With the expeditious growth and increasing complexity of 5G network infrastructure, identifying the behavior of 5G network’s core functional modules and their operations is getting increasingly strategic to enforce an adequate degree of Quality of Service (QoS). Existing network monitoring and management tools and techniques approach the issue of QoS from a data plane perspective and do not consider the core modules and core network operations. The thesis addresses this significant research gap and tries to analyze and identify the anomalies in the core metrics generated by Network Function (NF) modules such as AMF, AUSF, and UDM during the User Equipment (UEs) registration process.
We collect the CPU and memory metrics from these core 5G modules during the registration processes to identify the stress-anomalous behavior of these Network Functions (NFs) using Deep Learning algorithms.
The Deep Learning algorithm applies neuron-like structures for learning, mimicking the human neural system, and has changed the strategy of learning a task and automating it. It is only relevant that this method should be investigated and harnessed for the behavioral analysis of core networking modules of the 5G core. The required datasets for the experiments are collected from a simulated environment keeping Open 5G Core (O5GC) as the 5G network core, and the Benchmarking tool associated with O5GC simulates registration operations of UEs. The approach is to view the problem as a binary classification where the Deep Learning model identifies the anomalous behavior of the core network during the UE registration process using core metrics.
The research engages a discriminative approach to learning the distinction between the normal and stressed anomalous data after observing O5GC core behavior. The thesis employs a supervised learning approach that allows the detection of anomalies in the core network. Convolutional neural network (CNN) model Multi-Layer Perceptron (MLP) and to predict the anomalies in the dataset.