The main aim of this dissertation is to address the security issues of the physical layer of Cyber Physical Systems. The network security is first assessed using a 5-level Network Security Evaluation Scheme (NSES).
The network security is then enhanced using a novel Intrusion Detection System that is designed using Supervised Machine Learning. Defined as a complete architecture, this framework includes a complete packet analysis of radio traffic of Routing Protocol for Low-Power and Lossy Networks (RPL). A dataset of 300 different simulations of RPL network is defined for normal traffic, hello flood attack, DIS attack, increased version attack and decreased rank attack. The IDS is a multi-model detection model that provides an efficient detection against the known as well as new attacks.
The model analysis is done with the cross-validation method as well as using the new data from a similar network. To detect the known attacks, the model performed at 99% accuracy rate and for the new attack, 85% accuracy is achieved. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/11675 |
| Date | 08 April 2020 |
| Creators | Sharma, Mridula |
| Contributors | Gebali, Fayez, Elmiligi, Haytham |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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