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A Model for Predicting the Likelihood of Successful Exploitation

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Title:A Model for Predicting the Likelihood of Successful Exploitation
Authors:Holm, Hannes
Rodhe, Ioana
Keywords:Machine Learning and Cyber Threat Intelligence and Analytics
cyber security
experiments
exploits
machine learning
Date Issued:07 Jan 2020
Abstract:This 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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/64531
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.789
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Cyber Threat Intelligence and Analytics


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