Detecting Network Degradation Using Machine Learning Predicting abnormal network behavior with anomaly detection
dc.contributor.author | Rojas, Adrian Gashi | |
dc.contributor.author | Nordholm, Niclas Ogeryd | |
dc.contributor.department | Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers) | sv |
dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers) | en |
dc.date.accessioned | 2019-07-03T14:39:32Z | |
dc.date.available | 2019-07-03T14:39:32Z | |
dc.date.issued | 2017 | |
dc.description.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. | |
dc.identifier.uri | https://hdl.handle.net/20.500.12380/253308 | |
dc.language.iso | eng | |
dc.setspec.uppsok | Technology | |
dc.subject | Data- och informationsvetenskap | |
dc.subject | Computer and Information Science | |
dc.title | Detecting Network Degradation Using Machine Learning Predicting abnormal network behavior with anomaly detection | |
dc.type.degree | Examensarbete för masterexamen | sv |
dc.type.degree | Master Thesis | en |
dc.type.uppsok | H | |
local.programme | Computer science – algorithms, languages and logic (MPALG), MSc |
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