Random Forest-based detection of cyber-attacks in substation automation systems in the context of IEC 61850 GOOSE communication protocol

The development of the Smart Grid aims to improve the operation of the traditional grid through the incorporation of information and communication technology. This is typically done through the integration of communication networks and a set of protocols that make the electricity grid prone to cyberattacks. Cyberattack threats such as data manipulation and replay attacks typically target substation automation systems and hence causing severe damage to the electricity grid assets leading to significant economic loss. In order to make the smart grid more resilient to such cyberattacks, it is critical to detect such cyberattacks accurately. The work presented in this thesis looks into machine learning techniques and in particular the Random Forest as an ensemble classifier to detect and classify the cyberattacks from other power quality disturbances and normal operation. Furthermore, the thesis addresses the issue of identifying the key features that effectively help in detecting such cyberattacks.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Jose, Kripa Mary

Advisor dc:contributor.advisor

• Ibrahim, Walid Morsi

• IEC 61850 protocol
• Intelligent electronic devices
• Random Forest
• Smart grid
• Cybersecurity

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1571

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1571