Graduation Year
2018
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Yao Liu, Ph.D.
Committee Member
Jay Ligatti, Ph.D.
Committee Member
Simon Ou, Ph.D.
Committee Member
Dan Shen, Ph.D.
Committee Member
Richard Gitlin, Sc.D.
Keywords
algorithms, cyber-physical systems, cybersecurity, online services
Abstract
This dissertation treats a variety of topics in the computer security domain which have direct impact on everyday life. The first extends false data injection attacks against state estimation in electric power grids and then provides a novel power flow model camouflage method to hamper these attacks. The second deals with automotive theft response, detailing a method for a car to intelligently identify when it has been stolen, based on collected behavioral traits of its driver. The third demonstrates a new attack against the content integrity of the PDF file format, caus- ing humans and computers to see different information within the same PDF documents. This dissertation lastly describes some future work efforts, identifying some potential vulnerabilities in the automated enforcement of copyright protection for audio (particularly music) in online systems such as YouTube.
Scholar Commons Citation
Markwood, Ian, "Offensive and Defensive Security for Everyday Computer Systems" (2018). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/7336
