Detecting Network Degradation Using Machine Learning Predicting abnormal network behavior with anomaly detection

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/253308
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Type: Examensarbete för masterexamen
Master Thesis
Title: Detecting Network Degradation Using Machine Learning Predicting abnormal network behavior with anomaly detection
Authors: Rojas, Adrian Gashi
Nordholm, Niclas Ogeryd
Abstract: 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.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2017
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/253308
Collection:Examensarbeten för masterexamen // Master Theses



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