A Model for Predicting the Likelihood of Successful Exploitation

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dc.contributor.authorHolm, Hannes
dc.contributor.authorRodhe, Ioana
dc.date.accessioned2020-01-04T08:31:22Z
dc.date.available2020-01-04T08:31:22Z
dc.date.issued2020-01-07
dc.description.abstractThis paper presents a model that estimates the likelihood that a detected vulnerability can be exploited. The data used to produce the model was obtained by carrying out an experiment that involved exploit attempts against 1179 different machines within a cyber range. Three machine learning algorithms were tested: support vector machines, random forests and neural networks. The best results were provided by a random forest model. This model has a mean cross-validation accuracy of 98.2% and an F1 score of 0.73.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.789
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64531
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning and Cyber Threat Intelligence and Analytics
dc.subjectcyber security
dc.subjectexperiments
dc.subjectexploits
dc.subjectmachine learning
dc.titleA Model for Predicting the Likelihood of Successful Exploitation
dc.typeConference Paper
dc.type.dcmiText

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