FINDING ANOMALOUS TIME FRAMES IN REAL-WORLD LOG DATA

Abstract: Anomaly detection is a huge fi€eld of research focused on the task of €finding weird or outlying points in data. Th‘is task is useful in all fi€elds that handle large amounts of data and is therefore a big topic of research. Th‘e focus of research often lies in fi€nding novel approaches for €finding anomalies in already labeled and well-understood data. ‘This thesis will not focus on a novel algorithm but instead display and discuss the power of an anomaly detection process that focuses on feature engineering and feature exploration. Th‘e thesis will also compare two unsupervised anomaly classifi€cation algorithms, namely k-nearest neighbours and principal component analysis, in terms of explainability and scalability. ‘The results concludes that sometimes feature engineering can display anomalies just as well as novel and complex anomaly detection algorithms.