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dc.contributor.authorZabarah, Saif
dc.date.accessioned2023-01-02 21:31:19 (GMT)
dc.date.available2023-01-02 21:31:19 (GMT)
dc.date.issued2023-01-02
dc.date.submitted2022-12-15
dc.identifier.urihttp://hdl.handle.net/10012/19008
dc.description.abstractWe present Soteria, a data processing pipeline for detecting multi-institution attacks. Multi-institution attacks contact large number of potential targets looking for vulnerabilities that span multiple institutions. Soteria uses a set of Machine Learning techniques to detect future attacks, predict their future targets, and ranks attacks based on their predicted severity. Our evaluation with real data from Canada wide institutions networks shows that Soteria can predict future attacks with 95% recall rate, predict the next targets of an attack with 97% recall rate, and can detect attacks in the first 20% of their life span. Soteria is deployed in production at CANARIE Canada wide network that connects tens of Canadian academic institutions.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectcybersecurityen
dc.subjectsystemsen
dc.titleSoteria: An Approach for Detecting Multi-Institution Attacksen
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Mathematicsen
uws-etd.embargo.terms0en
uws.contributor.advisorBoutaba, Raouf
uws.contributor.advisorAl-Kiswany, Samer
uws.contributor.affiliation1Faculty of Mathematicsen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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