#11693. A multiclass hybrid approach to estimating software vulnerability vectors and severity score

August 2026publication date
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Journal’s subject area:
Safety Research;
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Abstract:
Classifying detected software vulnerabilities is an important process. However, the metric values of security vectors are manually determined by humans, which takes time and may introduce errors stemming from human nature. It is necessary to use machine learning algorithms and data mining techniques to improve the quality and speed of vulnerability analysis and discovery processes. However, studies in this area are still limited. In this study, vulnerability vectors were estimated using the natural language processing techniques bag of words, term frequency–inverse document frequency, and n-gram for feature extraction together with various multiclass classification algorithms. Our experiments using a large public dataset facilitate assessment and provide a standard-compliant prediction model for classifying software vulnerability vectors. The results show that the joint use of different techniques and classification algorithms is a promising solution to a multi-probability and difficult-to-predict problem.
Keywords:
Information security; Multiclass classification; Software security; Software vulnerability; Text analysis

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