Location

Grand Wailea, Hawaii

Event Website

https://hicss.hawaii.edu/

Start Date

7-1-2020 12:00 AM

End Date

10-1-2020 12:00 AM

Description

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.

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Jan 7th, 12:00 AM Jan 10th, 12:00 AM

A Model for Predicting the Likelihood of Successful Exploitation

Grand Wailea, Hawaii

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.

https://aisel.aisnet.org/hicss-53/st/cyber_threat_intelligence/2