Detecting Network Degradation Using Machine Learning Predicting abnormal network behavior with anomaly detection
Ladda ner
Typ
Examensarbete för masterexamen
Master Thesis
Master Thesis
Program
Computer science – algorithms, languages and logic (MPALG), MSc
Publicerad
2017
Författare
Rojas, Adrian Gashi
Nordholm, Niclas Ogeryd
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Due to the prohibitive cost of downtime in large complex systems, it is important to reduce or entirely eliminate any downtime that might happen as a result of degradation in system quality. This thesis paper presents a newly developed model for network congestion detection in large scale networks, using Gaussian Mixture Model and Isolation Forest algorithms to improve early detection of failure in the Ericsson EBM data. The results from the evaluation suggests that the developed method is more robust and precise in finding anomalies, compared to the current method used by Ericsson.
Beskrivning
Ämne/nyckelord
Data- och informationsvetenskap , Computer and Information Science