Date of Award
12-2021
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical Engineering
Committee Chair/Advisor
Dr. Johan Enslin
Committee Member
Dr. Bill Suski
Committee Member
Dr. Harlan Russell
Abstract
The cybersecurity of power systems is jeopardized by the threat of spoofing and man-in-the-middle style attacks due to a lack of physical layer device authentication techniques for operational technology (OT) communication networks. OT networks cannot support the active probing cybersecurity methods that are popular in information technology (IT) networks. Furthermore, both active and passive scanning techniques are susceptible to medium access control (MAC) address spoofing when operating at Layer 2 of the Open Systems Interconnection (OSI) model. This thesis aims to analyze the role of deep learning in passively authenticating Ethernet devices by their communication signals. This method operates at the physical layer or Layer 1 of the OSI model. The security model collects signal data from Ethernet device transmissions, applies deep learning to gather distinguishing features from signal data, and uses these features to make an authentication decision on the Ethernet devices. The proposed approach is passive, automatic, and spoof-resistant. The role of deep learning is critical to the security model. This thesis will look at analyzing and improving deep learning at each step of the security model including data processing, model training, model efficiency, transfer learning on new devices, and device authentication.
Recommended Citation
Torlay, Lucas, "Analysis of Deep Learning Methods for Wired Ethernet Physical Layer Security of Operational Technology" (2021). All Theses. 3696.
https://open.clemson.edu/all_theses/3696
Included in
Data Science Commons, Information Security Commons, Other Electrical and Computer Engineering Commons, Power and Energy Commons, Signal Processing Commons, Systems and Communications Commons