5G and Related Network Infrastructure CVE-Annotated Dataset: Distinguishing 5G Native, LTE, Auxiliary to 5G, and Non-5G Vulnerabilities

Dataset Description The dataset was generated using the source code available at https://doi.org/10.5281/zenodo.17572825 and was subsequently manually annotated. Starting from a selection of CVEs selected on the basis of a keyword whitelist of terms inherent to 5G, the dataset includes 1,531 annotated CVE entries retrieved from the NIST NVD, covering the years 2019 through 2025, classified into four labels: 5G: if the vulnerability directly impacts 5G infrastructures, protocols, or specific 5G components, it receives the ”5g” label, indicating direct relevance to 5G security. auxiliary: if the vulnerability has indirect implications for 5G systems, such as those affecting shared infrastructure, common protocols, or components that bridge LTE and 5G networks, it is labeled ”auxiliary”. lte: if the vulnerability does not directly affect 5G networks but is specific to LTE, it is classified ”lte”, representing legacy 4G vulnerabilities without 5G implications. no5G: if the vulnerability demonstrates no relationship to 5G technology, either directly or indirectly, the ”no5g” category is assigned. In the following table, the frequency of the labels is presented: Label Frequency 5g 255 auxiliary 169 lte 95 no5G 1012 The dataset exhibits a significant class imbalance, with varying distributions across the four classification categories. This imbalance reflects the real-world distribution of vulnerabilities but may pose challenges for machine learning model training and evaluation. To address the class imbalance issue and facilitate binary classification tasks, a balanced version of the dataset is also provided as an additional column of the CSV file. This balanced subset comprises 255 samples for the 5G class and 255 samples for the no5G class, totaling 510 entries.