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

dc.contributor.authorRojas, Adrian Gashi
dc.contributor.authorNordholm, Niclas Ogeryd
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:39:32Z
dc.date.available2019-07-03T14:39:32Z
dc.date.issued2017
dc.description.abstractDue 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.urihttps://hdl.handle.net/20.500.12380/253308
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleDetecting Network Degradation Using Machine Learning Predicting abnormal network behavior with anomaly detection
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
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