Manually analysing logfiles is a very time consuming and error-prone effort. By developing a system to automatically analysing the logfiles it is possible to both increase the speed and accuracy of the analysis. This thesis presents a method for automatic anomaly detection in logfiles using statistical analysis and threshold based classification. The presented method uses five different threshold based approaches to identify anomalous entries within a logfile. Each of the five approaches was successful in identifying and reporting perceived anomalies within 805 logfiles provided by Sandvine, it was however not possible to do a formal evaluation of the results due to a lack of a ground truth.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kau-72931 |
| Date | January 2019 |
| Creators | Larsson, Daniel |
| Publisher | Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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