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dc.contributor.authorFazio, Peppino
dc.contributor.authorTropea, Mauro
dc.contributor.authorVozňák, Miroslav
dc.contributor.authorDe Rango, Floriano
dc.date.accessioned2021-02-03T08:52:56Z
dc.date.available2021-02-03T08:52:56Z
dc.date.issued2020
dc.identifier.citationComputer Networks. 2020, vol. 182, art. no. 107464.cs
dc.identifier.issn1389-1286
dc.identifier.issn1872-7069
dc.identifier.urihttp://hdl.handle.net/10084/142633
dc.description.abstractFrom many years, the methods to defend against Denial of Service attacks have been very attractive from different point of views, although network security is a large and very complex topic. Different techniques have been proposed and so-called packet marking and IP tracing procedures have especially demonstrated a good capacity to face different malicious attacks. While host-based DoS attacks are more easily traced and managed, network-based DoS attacks are a more challenging threat. In this paper, we discuss a powerful aspect of the IP traceback method, which allows a router to mark and add information to attack packets on the basis of a fixed probability value. We propose a potential method for modeling the classic probabilistic packet marking algorithm as Markov chains, allowing a closed form to be obtained for evaluating the correct number of received marked packets in order to build a meaningful attack graph and analyze how marking routers must behave to minimize the overall overhead.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesComputer Networkscs
dc.relation.urihttp://doi.org/10.1016/j.comnet.2020.107464cs
dc.rights© 2020 Published by Elsevier B.V.cs
dc.subjectprobabilistic packet markingcs
dc.subjectIP tracebackcs
dc.subjectstochastic processcs
dc.subjectDoS attackcs
dc.subjectnetwork securitycs
dc.subjectmarking probabilitycs
dc.titleOn packet marking and Markov modeling for IP Traceback: A deep probabilistic and stochastic analysiscs
dc.typearticlecs
dc.identifier.doi10.1016/j.comnet.2020.107464
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume182cs
dc.description.firstpageart. no. 107464cs
dc.identifier.wos000588436000009


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