Pattern Mining and Recognition in 5G Network Traffic Using Time Series Clustering

Sammanfattning

The adoption of 5G mobile networks is changing the way we connect our world. Now, it is not just phones that are connected to the network, it is everything - smart homes, self-driving cars, factory equipment, and anything in between. Because of this, there has been a large increase in the volume and complexity of mobile network traffic in recent years. As 5G becomes more widely adopted, this trend will continue moving forward. This presents a problem for mobile network operators. To account for this increase in traffic volume and complexity, the network must be optimized to handle it. However, the only way to do this is to better understand the traffic sent over the network. As such, the companies building and operating these networks rely on models that can define a set of traffic profiles from real-world network data. This thesis presents a novel method of identifying traffic profiles from 5G network data by analyzing the network traffic as unstructured time series data. Using two datasets containing TCP and UDP traffic data with 10 million time series apiece, clusters were defined for each using time series clustering techniques. Specifically, the ROCKET family of algorithms was adapted for clustering purposes, applying k-means clustering on top of the ROCKET feature transformations. The resulting clusters were analyzed and compared to another clustering model - one based on summary statistics from each time series. Overall, the ROCKET models appeared to produce more coherent traffic profiles compared to the baseline clustering model, and the proposed framework shows great promise - not just in network traffic clustering, but any analysis of unstructured time series data.