By using machine learning to monitor and find deviations in log data makes it easier for developers and can prevent a workflow from stopping. The goal of this project is to investigate if it is possible to find anomalies in log data using reinforcement learning. An anomaly detection model with reinforcement learning is compared to a machine learning method traditionally used for anomaly detection. The results show that reinforcement learning has an opportunity for a better or similar result as the traditional machine learning method.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kau-82670 |
| Date | January 2021 |
| Creators | Lantz, Sofia |
| Publisher | Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| 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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