A Model for Predicting the Likelihood of Successful Exploitation
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Date
2020-01-07
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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.
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Machine Learning and Cyber Threat Intelligence and Analytics, cyber security, experiments, exploits, machine learning
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10 pages
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Proceedings of the 53rd Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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