Abstract

In the past decade, cyber operations have been increasingly utilized to further policy goals of state-sponsored actors to shift the balance of politics and power on a global scale. One of the ways this has been evidenced is through the exploitation of electric grids via cyber means. A remote tripping command injection attack is one of the types of attacks that could have devastating effects on the North American power grid. To better understand these attacks and create detection axioms to both quickly identify and mitigate the effects of a remote tripping command injection attack, a dataset comprised of 128 variables (primarily synchrophasor measurements) was analyzed via statistical methods and machine learning algorithms in RStudio and WEKA software respectively. While statistical methods were not successful due to the non-linearity and complexity of the dataset, machine learning algorithms surpassed accuracy metrics established in previous research given a simplified dataset of the specified attack and normal operational data. This research allows future cybersecurity researchers to better understand remote tripping command injection attacks in comparison to normal operational conditions. Further, an incorporation of the analysis has the potential to increase detection and thus mitigate risk to the North American power grid in future work.

Graduation Date

2018

Semester

Summer

Advisor

Caulkins, Bruce

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Degree Program

Modeling & Simulation

Format

application/pdf

Identifier

CFE0007249

URL

http://purl.fcla.edu/fcla/etd/CFE0007257

Language

English

Release Date

August 2018

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

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