This thesis explores how a detection engine using Artificial Neural Networks (ANNs) could be implemented within the DETECT framework. The framework is used for security purposes in Cyber-physical systems. These are critical systems often vital to important infrastructure so discovering new ways of how to defend against threats is of huge importance. However, there are many difficult challenges that needs to be addressed before employing an ANN as a threat detection mechanism. Most notable what kind of ANN to use, training data and issues such as over-fitting. These challenges were addressed in the model that was created for this paper. The model was based on the current literature and previous research. To make informed decisions about the model a literature review was carried out to gather as much information as possible. A key insight from the review was the use of recurrent neural networks for threat detection.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-105161 |
| Date | January 2021 |
| Creators | Anjel, Elise, Bäckström, Samuel |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| 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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