A Model for Predicting the Likelihood of Successful Exploitation
Hannes Holm, Ioana Rodhe · Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 2020
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.