Intrusion detection and response for system and network attacks

dc.contributor.advisor Johnny S. Wong
dc.contributor.author Stanley, Fred
dc.contributor.department Department of Computer Science
dc.date 2018-08-11T08:01:03.000
dc.date.accessioned 2020-06-30T02:30:14Z
dc.date.available 2020-06-30T02:30:14Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2009
dc.date.embargo 2013-06-05
dc.date.issued 2009-01-01
dc.description.abstract <p>This work focuses on Intrusion Detection System (IDS) and Intrusion Response System (IRS) model for system and network attacks. For decades, IDS has evolved tremendously and has become highly sophisticated. However, the response to an attack is still manually triggered by an administrator who relies on static mapping to counteract the intrusion. The speed of attack-spread and its increased complexities in recent years have shown that it is highly critical to develop an automatic IRS. Moreover, manual responses are not flexible and effective in distributed environment without infrastructure.</p> <p>This work presents a cost based response model that is tightly coupled with multi-source IDS. It is a known fact that any system can be broken down into smaller granules of services and resources. A dependency graph is employed to describe the relations between services and resources in a system. This dependency graph is also used to propagate the total value of the system down to the service and resource levels. The damage cost of the intrusion and the response cost of the responses are evaluated using the dependency graph. Using several performance metrics, a response which brings the most benefit to the system is deployed.</p> <p>We demonstrate the abilities of our model by using buffer overflow attack caused by a computer worm on Optimized Link State Routing (OLSR) protocol on a wireless ad-hoc network environment. Experimental results show that our model is effective and is highly practical.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/10684/
dc.identifier.articleid 1730
dc.identifier.contextkey 2806928
dc.identifier.doi https://doi.org/10.31274/etd-180810-284
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/10684
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/24890
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/10684/Stanley_iastate_0097M_10522.pdf|||Fri Jan 14 18:25:53 UTC 2022
dc.subject.disciplines Computer Sciences
dc.subject.keywords Automated response model
dc.subject.keywords Dependency graph
dc.subject.keywords Generic response model
dc.subject.keywords Intrusion detection and response
dc.subject.keywords OLSR worm
dc.subject.keywords snort
dc.title Intrusion detection and response for system and network attacks
dc.type thesis en_US
dc.type.genre thesis en_US
dspace.entity.type Publication
relation.isOrgUnitOfPublication f7be4eb9-d1d0-4081-859b-b15cee251456
thesis.degree.level thesis
thesis.degree.name Master of Science
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