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Modeling and simulations of worms and mitigation techniques

Posted on:2008-07-12Degree:Ph.DType:Thesis
University:Georgia Institute of TechnologyCandidate:Abdelhafez, MohamedFull Text:PDF
GTID:2448390005950170Subject:Engineering
Abstract/Summary:
Internet worm attacks have become increasingly more frequent and have had a major impact on the economy, making the detection and prevention of these attacks a top security concern. Several counter-measures have been proposed and evaluated in recent literature. However, the effect of these proposed defensive mechanisms on legitimate competing traffic has not been analyzed.; The first contribution of this thesis is a comparative analysis of the effectiveness of several of these proposed mechanisms, including a measure of their effect on normal web browsing activities. In addition, we introduce a new defensive approach that can easily be implemented on existing hosts, and which significantly reduces the rate of spread of worms using TCP connections to perform the infiltration. Our approach has no measurable effect on legitimate traffic.; The second contribution is presenting a variant of the flash worm that we term Compact Flash or CFlash that is capable of spreading even faster than its predecessor. We perform a comparative study between the flash worm and the CFlash worm using a full-detail packet-level simulator, and the results show the increase in propagation rate of the new worm given the same set of parameters.; The third contribution is the study of the behavior of TCP based worms in MANETs. We develop an analytical model for the worm spread of TCP worms in the MANETs environment that accounts for payload-size, bandwidth-sharing, radio range, nodal density and several other parameters specific for MANET topologies. We also present numerical solutions for the model and verify the results using packet-level simulations. The results show that the analytical model developed here matches the results of the packet-level simulation in most cases.
Keywords/Search Tags:Worm, Model, Results
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