#6163. A multiclass hybrid approach to estimating software vulnerability vectors and severity score

September 2026publication date
Proposal available till 12-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Safety, Risk, Reliability and Quality;
Computer Networks and Communications;
Software;
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More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
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. These metrics are important because of their role in the calculation of vulnerability severity. 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, namely Na?ve Bayes, decision tree, k-nearest neighbors, multilayer perceptron, and random forest. Our experiments using a large public dataset facilitate assessment and provide a standard-compliant prediction model for classifying software vulnerability vectors.
Keywords:
Information security; Multiclass classification; Software security; Software vulnerability; Text analysis

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