Anomaly Detection in Computer Networks
Abstract: In this degree project, we study the anomaly detection problem in log files of computer networks. In particular, we try to find an efficient way to detect anomalies in our data, which consist of different logging messages from different systems in CERN’s network for the LHC-b experiment. The contributions of the thesis are double: 1) The thesis serves as a survey on how we can detect threats, and errors in systems that are logging a huge amount of messages in the databases of a computer network. 2) Scientists in the LHC-b experiment make use of the Elasticsearch, which is an open source search engine and logging platform with great reputation, providing log monitoring, as well as data stream processing. Moreover, the Elasticsearch provides a machine learning feature that automatically models the behavior of the data, learning trends, and periodicity to identify anomalies. Alternatively to the Elasticsearch machine learning feature, we build, test and evaluate some machine learning models that can be used for the same purpose from the scientists of the experiment. We further provide results that our models generalize well to unseen log messages in the database.
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