Intrusion detection models based on data mining techniques in high density attacks scenario

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dc.contributor.advisor Focardi, Riccardo it_IT
dc.contributor.author Pistolato, Michele <1981> it_IT
dc.date.accessioned 2020-02-17 it_IT
dc.date.accessioned 2020-06-16T05:24:35Z
dc.date.issued 2020-03-13 it_IT
dc.identifier.uri http://hdl.handle.net/10579/16225
dc.description.abstract Intrusion detection systems represent a fundamental component at the basis of the information security of a computer network. The increasing improvements in the field of data mining, due to the capabilities of the modern computers to treat large amount of data, allow to evolve intrusion detection systems, with particular reference to those based on anomaly detection. Usually this kind of systems operate in networks where the illicit activities represent sporadic events that deviates from the normal licit usage. This dissertation describes a different situation resulting from an ethical hacking contest where attack attempts, generated by a large number of different subjects, represent the vast majority of the network traffic in comparison with the normal activity consisting in regular traffic carried out by known trusted entities. In this atypical reversed scenario, raw network traffic has been collected,analysed and suitable transformed in order to find relevant characteristics. Subsequently, the resulting data has been analysed through unsupervised data mining techniques in order to build models able to recognize licit traffic and the different attack patterns used. The results and the relative model efficiency has been measured, compared and discussed. The experiment and the resulting models represent a possible approach in anomaly detection field with particular regard to Operation Technology (OT) networks where licit traffic is generated by trusted and well known devices. Furthermore, these models can be adopted to analyse and compare different strategies used by different attackers toward various network targets. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Michele Pistolato, 2020 it_IT
dc.title Intrusion detection models based on data mining techniques in high density attacks scenario it_IT
dc.title.alternative Intrusion detection models based on data mining techniques in a high density attack scenario it_IT
dc.type Bachelor Thesis it_IT
dc.degree.name Informatica - computer science it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Scienze Ambientali, Informatica e Statistica it_IT
dc.description.academicyear 2018/2019, sessione straordinaria it_IT
dc.rights.accessrights closedAccess it_IT
dc.thesis.matricno 816113 it_IT
dc.subject.miur INF/01 INFORMATICA it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend 10000-01-01
dc.provenance.upload Michele Pistolato (816113@stud.unive.it), 2020-02-17 it_IT
dc.provenance.plagiarycheck Riccardo Focardi (focardi@unive.it), 2020-03-02 it_IT


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