{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,5]],"date-time":"2024-05-05T00:27:09Z","timestamp":1714868829964},"reference-count":18,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2024,5,1]]},"DOI":"10.1587\/transinf.2023dap0013","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T22:20:52Z","timestamp":1714515652000},"page":"674-682","source":"Crossref","is-referenced-by-count":0,"title":["Automated Labeling of Entities in CVE Vulnerability Descriptions with Natural Language Processing"],"prefix":"10.1587","volume":"E107.D","author":[{"given":"Kensuke","family":"SUMOTO","sequence":"first","affiliation":[{"name":"Waseda University"}]},{"given":"Kenta","family":"KANAKOGI","sequence":"additional","affiliation":[{"name":"Waseda University"}]},{"given":"Hironori","family":"WASHIZAKI","sequence":"additional","affiliation":[{"name":"Waseda University"}]},{"given":"Naohiko","family":"TSUDA","sequence":"additional","affiliation":[{"name":"Waseda University"}]},{"given":"Nobukazu","family":"YOSHIOKA","sequence":"additional","affiliation":[{"name":"Waseda University"}]},{"given":"Yoshiaki","family":"FUKAZAWA","sequence":"additional","affiliation":[{"name":"Waseda University"}]},{"given":"Hideyuki","family":"KANUKA","sequence":"additional","affiliation":[{"name":"Hitachi, Ltd."}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] The MITRE, \u201cCommon Vulnerabilities and Exposures (CVE),\u201d [no date info]. https:\/\/cve.mitre.org\/ (accessed: 2022-1-12)."},{"key":"2","unstructured":"[2] J. Evans, \u201cKey details phrasing,\u201d [no date info]. http:\/\/cveproject.github.io\/docs\/content\/key-details-phrasing.pdf (accessed: 2022-1-9)."},{"key":"3","unstructured":"[3] CVE Details, \u201cCVE Details,\u201d [no date info]. https:\/\/www.cvedetails.com\/ (accessed: 2022-3-15)."},{"key":"4","unstructured":"[4] J. Devlin, M.W. Chang, K. Lee, and K. Toutanova, \u201cBert: Pre-training of deep bidirectional transformers for language understanding,\u201d arXiv preprint arXiv:1810.04805, 2018."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] K. Sumoto, K. Kanakogi, H. Washizaki, N. Tsuda, N. Yoshioka, Y. Fukazawa, and H. Kanuka, \u201cAutomatic labeling of the elements of a vulnerability report cve with nlp,\u201d 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI), pp.164-165, IEEE, 2022. 10.1109\/iri54793.2022.00045","DOI":"10.1109\/IRI54793.2022.00045"},{"key":"6","unstructured":"[6] J. Sun, Z. Xing, H. Guo, D. Ye, X. Li, X. Xu, and L. Zhu, \u201cGenerating Informative CVE Description From ExploitDB Posts by Extractive Summarization,\u201d arXiv preprint arXiv:2101.01431, 2021."},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] C. Gao, X. Zhang, and H. Liu, \u201cData and knowledge-driven named entity recognition for cyber security,\u201d Cybersecurity, vol.4, no.1, pp.1-13, 2021. 10.1186\/s42400-021-00072-y","DOI":"10.1186\/s42400-021-00072-y"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] X. Wang, X. Liu, S. Ao, N. Li, Z. Jiang, Z. Xu, Z. Xiong, M. Xiong, and X. Zhang, \u201cDNRTI: A Large-Scale Dataset for Named Entity Recognition in Threat Intelligence,\u201d 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp.1842-1848, IEEE, 2020. 10.1109\/trustcom50675.2020.00252","DOI":"10.1109\/TrustCom50675.2020.00252"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] G. Yang, S. Dineen, Z. Lin, and X. Liu, \u201cFew-sample named entity recognition for security vulnerability reports by fine-tuning pre-trained language models,\u201d International Workshop on Deployable Machine Learning for Security Defense, pp.55-78, Springer, 2021. 10.1007\/978-3-030-87839-9_3","DOI":"10.1007\/978-3-030-87839-9_3"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] Y. Wei, L. Bo, X. Sun, B. Li, T. Zhang, and C. Tao, \u201cAutomated event extraction of cve descriptions,\u201d Information and Software Technology, vol.158, p.107178, 2023. 10.1016\/j.infsof.2023.107178","DOI":"10.1016\/j.infsof.2023.107178"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] H. Guo, S. Chen, Z. Xing, X. Li, Y. Bai, and J. Sun, \u201cDetecting and augmenting missing key aspects in vulnerability descriptions,\u201d ACM Transactions on Software Engineering and Methodology (TOSEM), vol.31, no.3, pp.1-27, 2022. 10.1145\/3498537","DOI":"10.1145\/3498537"},{"key":"12","unstructured":"[12] R.A. Bridges, C.L. Jones, M.D. Iannacone, K.M. Testa, and J.R. Goodall, \u201cAutomatic labeling for entity extraction in cyber security,\u201d arXiv preprint arXiv:1308.4941, 2013."},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] Z. Liu, Y. Xu, T. Yu, W. Dai, Z. Ji, S. Cahyawijaya, A. Madotto, and P. Fung, \u201cCrossner: Evaluating cross-domain named entity recognition,\u201d Proc. AAAI Conference on Artificial Intelligence, vol.35, no.15, pp.13452-13460, 2021. 10.1609\/aaai.v35i15.17587","DOI":"10.1609\/aaai.v35i15.17587"},{"key":"14","unstructured":"[14] R. Tang, X. Han, X. Jiang, and X. Hu, \u201cDoes synthetic data generation of llms help clinical text mining?,\u201d arXiv preprint arXiv:2303.04360, 2023."},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] C.-E. Gonz\u00e1lez-Gallardo, E. Boros, N. Girdhar, A. Hamdi, J.G. Moreno, and A. Doucet, \u201cYes but.. can chatgpt identify entities in historical documents?,\u201d arXiv preprint arXiv:2303.17322, 2023.","DOI":"10.1109\/JCDL57899.2023.00034"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] J. Fan, Y. Li, S. Wang, and T.N. Nguyen, \u201cA C\/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries,\u201d Proc. 17th International Conference on Mining Software Repositories, pp.508-512, 2020. 10.1145\/3379597.3387501","DOI":"10.1145\/3379597.3387501"},{"key":"17","unstructured":"[17] Hugging Face, \u201chuggingface\/transformers\/examples\/pytorch\/token-classification\/,\u201d [no date info]. https:\/\/github.com\/huggingface\/transformers\/tree\/master\/examples\/pytorch\/token-classification (accessed: 2022-1-16)."},{"key":"18","unstructured":"[18] Hugging Face, \u201cseqeval,\u201d [no date info]. https:\/\/huggingface.co\/spaces\/evaluate-metric\/seqeval (accessed: 2022-6-6)."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/5\/E107.D_2023DAP0013\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T04:14:42Z","timestamp":1714796082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/5\/E107.D_2023DAP0013\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,1]]},"references-count":18,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023dap0013","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,1]]},"article-number":"2023DAP0013"}}