{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:18:19Z","timestamp":1765610299063,"version":"3.41.2"},"reference-count":24,"publisher":"World Scientific Pub Co Pte Ltd","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p> In response to the continuous sophistication of cyber threat actors, it is imperative to make the best use of cyber threat intelligence converted from structured or semi-structured data and Named Entity Recognition (NER) techniques that contribute to extracting critical cyber threat intelligence. To promote the NER research in Cyber Threat Intelligence (CTI) domain, we provide a Large Dataset for NER in Cyber Threat Intelligence (LDNCTI). On the LDNCTI corpus, we investigated the feasibility of mainstream transformer-based models in CTI domain. To settle the problem of unbalanced label distribution, we introduce a transformer-based model with a Triplet Loss based on metric learning and Sorted Gradient harmonizing mechanism (TSGL). Our experimental results show that the LDNCTI well represents critical threat intelligence and that our transformer-based model with the new loss function outperforms previous schemes on the Dataset for NER in Threat Intelligence (DNRTI) and the dataset for NER in Advanced Persistent Threats (APTNER). <\/jats:p>","DOI":"10.1142\/s0218126623502018","type":"journal-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T03:52:46Z","timestamp":1673322766000},"source":"Crossref","is-referenced-by-count":5,"title":["NER in Cyber Threat Intelligence Domain Using Transformer with TSGL"],"prefix":"10.1142","volume":"32","author":[{"given":"Yuhuang","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei Street, Nanjing 210094, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8574-1286","authenticated-orcid":false,"given":"Mang","family":"su","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei Street, Nanjing 210094, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuting","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei Street, Nanjing 210094, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, No. 200, Xiaolingwei Street, Nanjing 210094, P. R. 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Chinese Computational Linguistics","author":"Sun C.","key":"S0218126623502018BIB030"}],"container-title":["Journal of Circuits, Systems and Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218126623502018","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T08:24:10Z","timestamp":1689063850000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218126623502018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,6]]},"references-count":24,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.1142\/S0218126623502018"],"URL":"https:\/\/doi.org\/10.1142\/s0218126623502018","relation":{},"ISSN":["0218-1266","1793-6454"],"issn-type":[{"type":"print","value":"0218-1266"},{"type":"electronic","value":"1793-6454"}],"subject":[],"published":{"date-parts":[[2023,3,6]]},"article-number":"2350201"}}