Konstantinos Charmanas
ABSTRACT
Software vulnerabilities are weaknesses of specific products and versions that may lead in benefiting attackers to exploit such malfunctions, having a further goal to gain access to operating systems, devices and users’ data. As not all vulnerabilities constitute potential threats to such information, this research attempts to explore ways to the identify the ones that are possible to be exploited using only textual descriptions. The practical goal of the experiments is to examine future raw descriptions in order to predict whether the linked product is likely to be exploited or not. The ground truth examined is the existence or absence of references that report exploitation concepts of the related weaknesses. To meet our objectives, in this study, we make use of Natural Language Processing (NLP) techniques, feature evaluation filtering mechanisms and an oversampling method in order to adapt the raw texts into inputs to classification models and detect the most important terms. The results are promising as many constructed models provided an overall accepted accuracy based on the information of the collected dataset.
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Index Terms
- Predicting the existence of exploitation concepts linked to software vulnerabilities using text mining
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