MS (Master of Science)
Computer and Information Sciences
Date of Award
Committee Chair or Co-Chairs
Ghaith Husari, Brian Bennett
Power grids have rapidly evolved into Smart grids and are heavily dependent on Supervisory Control and Data Acquisition (SCADA) systems for monitoring and control. However, this evolution increases the susceptibility of the remote (VMs, VPNs) and physical interfaces (sensors, PMUs LAN, WAN, sub-stations power lines, and smart meters) to sophisticated cyberattacks. The continuous supply of power is critical to power generation plants, power grids, industrial grids, and nuclear grids; the halt to global power could have a devastating effect on the economy's critical infrastructures and human life.
Machine Learning and Deep Learning-based cyberattack detection modeling have yielded promising results when combined as a Hybrid with an Intrusion Detection System (IDS) or Host Intrusion Detection Systems (HIDs). This thesis proposes two cyberattack detection techniques; one that leverages Machine Learning algorithms and the other that leverages Artificial Neural networks algorithms to classify and detect the cyberattack data held in a foundational dataset crucial to network intrusion detection modeling. This thesis aimed to analyze and evaluate the performance of a Hybrid Machine Learning (ML) and a Hybrid Deep Learning (DL) during ingress packet filtering, class classification, and anomaly detection on a Smart grid network.
Dissertation - embargo
Aribisala, Adedayo, "Hybrid Machine and Deep Learning-based Cyberattack Detection and Classification in Smart Grid Networks" (2022). Electronic Theses and Dissertations. Paper 4024. https://dc.etsu.edu/etd/4024
Copyright by the authors.
Available for download on Thursday, June 15, 2023