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Vulnerability Testing In Wireless Ad-hoc Networks Using Incremental Adaptive Corrective Learning

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Advisor
Denzinger, Jörg
Author
Bergmann, Karel
Accessioned
2014-05-05T19:54:37Z
Available
2014-06-16T07:00:41Z
Issued
2014-05-05
Submitted
2014
Other
artificial intelligence
search
evolutionary algorithm
genetic algorithm
wireless network
ad-hoc network
testing
multi-agent systems
network protocol
simulation
adversary
security
Subject
Artificial Intelligence
Computer Science
Type
Thesis
Metadata
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Abstract
The testing of complex adaptive systems for emergent misbehaviours currently lacks any kind of automated support. In this thesis, we present Incremental Adaptive Corrective Learning, an evolutionary learning technique which automatically identifies vulnerabilities and performance-limiting behaviours induced by adversarially controlled agents inserted into a tested mobile ad-hoc network. Three case studies are presented, each demonstrating how Incremental Adaptive Corrective Learning is used to induce poor network performance in different mobile ad-hoc network applications.
Corporate
University of Calgary
Faculty
Graduate Studies
Doi
http://dx.doi.org/10.5072/PRISM/28666
Uri
http://hdl.handle.net/11023/1504
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