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- W4387271415 abstract "Data from vulnerability databases, such as Common Vulnerabilities and Exposures (CVE)s from National Vulnerability Database (NVD), are used by hundreds of security solutions around the globe and is crucial for gathering cyber threat information. However, other studies have shown that these vulnerability lists may contain errors and abnormalities that need to be thoroughly examined by security professionals. In order to effectively assess the newly added vulnerability text information, as well as to reduce the burden of specialists and the previous method's false negative rate. In this paper, we proposed a pretrained model known as fine-tuned BERT approach to classifying the severity of the vulnerabilities at a different level. Data from the NVD has been collected during the last 10 years, and NLP has preprocessed the data to produce important data. The efficacy of the suggested approach is tested with the classical ML, DL, and pretrained models. The suggested approach obtained 94% accuracy as compared to other approaches." @default.
- W4387271415 created "2023-10-03" @default.
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- W4387271415 date "2023-01-01" @default.
- W4387271415 modified "2023-10-03" @default.
- W4387271415 title "Vulnerability Classification Based on Fine-Tuned BERT and Deep Neural Network Approaches" @default.
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- W4387271415 doi "https://doi.org/10.1007/978-981-99-4717-1_24" @default.
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