Detecting network failures using principal component analysis

The dataset is first analyzed on a basic level by looking at the correlations between number of measurements and average download speed for every day. Second, our PCA-based methodology applied on the dataset, taking into account many factors, including the number of correlated measurements. The results from each analysis is compared and evaluated. Based on the results, we give insights to just how efficient the tested methods are and what improvements that can be made on the methods.This thesis investigates the efficiency of a methodology that first performs a Principal Component Analysis (PCA), followed by applying a threshold-based algorithm with a static threshold to detect potential network degradation and network attacks. Then a proof of concept of an online algorithm that is using the same methodology except for using training data to set the threshold is presented and analyzed. The analysis and algorithms are used on a large crowd-sourced dataset of Internet speed measurements, in this case from the crowd-based speed test application Bredbandskollen.se