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Measurements Associated With Learning More Secure Computer Configuration Parameters

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abstract
To defend against a cyber attack in which the attacker searches the network for vulnerable machines, most people will install some specific security software that cost some money, or download the newest patches for the softwares to avoid some vulnerabilities. Neither way can be efficient especially when attacks are always updated. Reconnaissance is the essential part of a cyber attack during which the attacker is to learn about vulnerabilities of the targeted machines, including credentials, software versions, and misconfigured settings. Moving target strategy then can be implemented specifically against this part of an attack. Theoretically, a change in configuration during the construction of an exploit will alter the computer such that the machine no longer contains the same vulnerabilities discovered during reconnaissance, thereby rendering the initial reconnaissance step ineffective.
subject
Genetic Alogrithm
Machine Learning
Moving Target
Resilience
contributor
Zhou, Xin (author)
John, David J (committee chair)
Fulp, Errin (committee member)
Gage, Don (committee member)
Turkett, William (committee member)
date
2015-06-23T08:35:48Z (accessioned)
2016-06-22T08:30:09Z (available)
2015 (issued)
degree
Computer Science (discipline)
embargo
2016-06-22 (terms)
identifier
http://hdl.handle.net/10339/57131 (uri)
language
en (iso)
publisher
Wake Forest University
title
Measurements Associated With Learning More Secure Computer Configuration Parameters
type
Thesis

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