Offensive and Defensive Security for Everyday Computer Systems
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...
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ndltd-USF-oai-scholarcommons.usf.edu-etd-85332019-10-04T05:04:17Z Offensive and Defensive Security for Everyday Computer Systems Markwood, Ian 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. 2018-06-29T07:00:00Z text application/pdf https://scholarcommons.usf.edu/etd/7336 https://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=8533&context=etd Graduate Theses and Dissertations Scholar Commons algorithms cyber-physical systems cybersecurity online services Computer Sciences |
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algorithms cyber-physical systems cybersecurity online services Computer Sciences |
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algorithms cyber-physical systems cybersecurity online services Computer Sciences Markwood, Ian Offensive and Defensive Security for Everyday Computer Systems |
description |
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. |
author |
Markwood, Ian |
author_facet |
Markwood, Ian |
author_sort |
Markwood, Ian |
title |
Offensive and Defensive Security for Everyday Computer Systems |
title_short |
Offensive and Defensive Security for Everyday Computer Systems |
title_full |
Offensive and Defensive Security for Everyday Computer Systems |
title_fullStr |
Offensive and Defensive Security for Everyday Computer Systems |
title_full_unstemmed |
Offensive and Defensive Security for Everyday Computer Systems |
title_sort |
offensive and defensive security for everyday computer systems |
publisher |
Scholar Commons |
publishDate |
2018 |
url |
https://scholarcommons.usf.edu/etd/7336 https://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=8533&context=etd |
work_keys_str_mv |
AT markwoodian offensiveanddefensivesecurityforeverydaycomputersystems |
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1719260284875440128 |