CyberNER-LLM: Cyber Threat Intelligence Named Entity Recognition With Large Language Model

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works

Emergent Abilities of Large Language Models

Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.07682 · Cited by 1,264 · see citing works

A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly

Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083 · see citing works

Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What MattersPreprint

Wang, B., Min, S., Deng, X., Shen, J., Wu, Y., Zettlemoyer, L., & Sun, H. (2022) · Annual Meeting of the Association for Computational Linguistics · doi:10.48550/arxiv.2212.10001 · Cited by 428 · see citing works

Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives

Sun, N., Ding, M., Jiang, J., Xu, W., Mo, X., Tai, Y., & Zhang, J. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3273282 · Cited by 335 · see citing works

GPT-NER: Named Entity Recognition via Large Language Models

Wang, S., Sun, X., Li, X., Ouyang, R., Wu, F. Q., Zhang, T., Li, J., Wang, G., & Chen, G. (2025) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2025.findings-naacl.239 · Cited by 287 · see citing works

Acing the IOC Game

Liao, X., Yuan, K., Wang, X., Li, Z., Xing, L., & Beyah, R. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2978315 · Cited by 278 · see citing works

A Survey on In-context Learning

Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Liu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2301.00234 · Cited by 248 · see citing works

GPT-RE: In-context Learning for Relation Extraction using Large Language Models

Wan, Z., Cheng, F., Mao, Z., Liu, Q., Song, H., Li, J., & Kurohashi, S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.214 · Cited by 199 · see citing works

How can organizations develop situation awareness for incident response: A case study of management practice

Ahmad, A., Maynard, S. B., Desouza, K. C., Kotsias, J., Whitty, M. T., & Baskerville, R. (2020) · Computers & Security · doi:10.1016/j.cose.2020.102122 · Cited by 164 · see citing works

ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTPreprint

Wei, X., Cui, X., Cheng, N., Wang, X., Zhang, X., Huang, S., Xie, P., Xu, J., Chen, Y., Zhang, M., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.10205 · Cited by 157 · see citing works

Strategically-motivated advanced persistent threat: Definition, process, tactics and a disinformation model of counterattack

Ahmad, A., Webb, J., Desouza, K. C., & Boorman, J. (2019) · Computers & Security · doi:10.1016/j.cose.2019.07.001 · Cited by 146 · see citing works

HinCTI: A Cyber Threat Intelligence Modeling and Identification System Based on Heterogeneous Information Network

Gao, Y., Li, X., Peng, H. M., Fang, B., & Yu, P. S. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2987019 · Cited by 127 · see citing works

Cyberthreat Detection from Twitter using Deep Neural Networks

Dionisio, N., Alves, F., Ferreira, P. M., & Bessani, A. (2019) · 2019 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852475 · Cited by 126 · see citing works

CASIE: Extracting Cybersecurity Event Information from Text

Satyapanich, T., Ferraro, F., & Finin, T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6401 · Cited by 119 · see citing works

Multi-Source and Multi-modal Deep Network Embedding for Cross-Network Node Classification

Yang, H., He, H. X., Zhang, W., Wang, Y., & Lin, J. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3653304 · Cited by 112 · see citing works

Naming entity recognition of citrus pests and diseases based on the BERT-BiLSTM-CRF model

Liu, Y., Wei, S., Huang, H., Lai, Q., Li, M., & Guan, L. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121103 · Cited by 98 · see citing works

Measuring and visualizing cyber threat intelligence quality

Schlette, D., Böhm, F., Caselli, M., & Pernul, G. (2020) · International Journal of Information Security · doi:10.1007/s10207-020-00490-y · Cited by 92 · see citing works

A review and theoretical explanation of the ‘Cyberthreat-Intelligence (CTI) capability’ that needs to be fostered in information security practitioners and how this can be accomplished

Shin, B., & Lowry, P. B. (2020) · Computers & Security · doi:10.1016/j.cose.2020.101761 · Cited by 90 · see citing works

DNRTI: A Large-Scale Dataset for Named Entity Recognition in Threat Intelligence

Wang, X., Liu, X., Ao, S., Li, N., Jiang, Z., Xu, Z., Xiong, Z., Xiong, M., & Zhang, X. (2020) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom50675.2020.00252 · Cited by 83 · see citing works

Deep learning for named entity recognition: a survey

Hu, Z., Hou, W. Z., & Liu, X. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09646-6 · Cited by 79 · see citing works

Adopting and integrating cyber-threat intelligence in a commercial organisation

Kotsias, J., Ahmad, A., & Scheepers, R. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2022.2088414 · Cited by 78 · see citing works

CDTier: A Chinese Dataset of Threat Intelligence Entity Relationships

Zhou, Y., Ren, Y., Yi, M., Xiao, Y., Tan, Z., Moustafa, N., & Tian, Z. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3240411 · Cited by 64 · see citing works

Collecting Cyber Threat Intelligence from Hacker Forums via a Two-Stage, Hybrid Process using Support Vector Machines and Latent Dirichlet Allocation

Deliu, I., Leichter, C., & Franke, K. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622469 · Cited by 62 · see citing works

KnowCTI: Knowledge-based cyber threat intelligence entity and relation extraction

Wang, G., Liu, P., Huang, J., Bin, H., Wang, X., & Zhu, H. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103824 · Cited by 47 · see citing works

How Can We Know When Language Models Know? On the Calibration of Language Models for Question AnsweringPreprint

Jiang, Z., Araki, J., Ding, H., & Neubig, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00955 · Cited by 39 · see citing works

Country-level cybersecurity posture assessment:Study and analysis of practices

Bahuguna, A., Bisht, R. K., & Pande, J. (2020) · Information Security Journal A Global Perspective · doi:10.1080/19393555.2020.1767239 · Cited by 27 · see citing works

CyberEyes: Cybersecurity Entity Recognition Model Based on Graph Convolutional Network

Fang, Y., Zhang, Y., & Huang, C. (2020) · The Computer Journal · doi:10.1093/comjnl/bxaa141 · Cited by 26 · see citing works

CyNER: A Python Library for Cybersecurity Named Entity RecognitionPreprint

Alam, T., Bhusal, D., Park, Y. H., & Rastogi, N. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2204.05754 · Cited by 25 · see citing works

Clustering APT Groups Through Cyber Threat Intelligence by Weighted Similarity Measurement

Chen, Z.-S., Vaitheeshwari, R., Wu, E. H., Lin, Y., Hwang, R., Lin, P., Lai, Y., & Ali, A. (2024) · IEEE Access · doi:10.1109/access.2024.3469552 · Cited by 17 · see citing works

P-ICL: Point In-Context Learning for Named Entity Recognition with Large Language ModelsPreprint

Jiang, G., Ding, Z., Shi, Y., & Yang, D. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.04960 · Cited by 7 · see citing works

TIMFuser: A multi-granular fusion framework for cyber threat intelligence

Ma, C., Jiang, Z., Zhang, K., Ling, Z., Jiang, J., You, Y., Yang, P., & Feng, H. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104141 · Cited by 6 · see citing works

Mitigating ransomware attacks through cyber threat intelligence and machine learning

Kante, M., Sharma, V., & Gupta, K. (2024) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v33.i3.pp1958-1965 · Cited by 4 · see citing works

InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated AnswersPreprint

Yehuda, Y., Malkiel, I., Barkan, O., Weill, J., Ronen, R., & Koenigstein, N. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.02889 · Cited by 2 · see citing works

DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity RecognitionPreprint

Luo, H., Jin, Y., Li, X., Liu, X., Chen, R., Shang, T., Wang, K., Wen, Q., & Liu, Z. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2409.11022 · Cited by 1 · see citing works

Agentic AI and Large Language Models for Autonomous IoT Cybersecurity: A Systematic Survey, Taxonomy, and Research Roadmap

Nageshwaran, V., & Ezekiel, S. (2026) · Electronics · doi:10.3390/electronics15122740 · Cited by 4 · see citing works

CyMapNER: a named entity recognition model for cyberspace surveying and mapping domain

Zhang, J., Shi, F., Xu, C., Li, Y., Yin, X., & Ge, M. (2026) · Cybersecurity · doi:10.1186/s42400-026-00578-3 · Cited by 1 · see citing works

LLM-Based Dual-Pipeline Extraction of IoCs and TTPs from Backdoor CTI Reports

Liu, B. F., Jiang, Z. H., Chen, L., Liu, Y., Wang, Q., Li, Y., Yao, Y., & Wang, S. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582506

A Survey of Cyberspace Security Event Extraction Techniques: Traditional, Machine Learning and Deep Learning Paradigms

Yang, C. R., Bao, J., Shang, Y., Ding, H., Guo, Q., Liao, Y., Zhou, R., & Xiong, N. N. (2026) · IET Communications · doi:10.1049/cmu2.70187

Knowledge-Enhanced Instruction-Tuned Generative Method for Cyber Threat Intelligence Named Entity Recognition

Li, J., Yang, L., & Che, B. (2026) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs69761.2026.11613446

Few-shot Named Entity Recognition in Cyber Threat Intelligence via Prompt-based Sentence Demonstrations

He, S., Wei, X., & He, F. (2026) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd68734.2026.11582741

INERCIA: An Advanced System for Cyberattack Detection through NLP using a Fine-Tuned LLM

Domínguez‐Jiménez, J. J., Medina‐Bulo, I., Boubeta-­Puig, J., Delgado‐Pérez, P., Palomo‐Lozano, F., Guerrero-Contreras, G., Balderas-Díaz, S., & Gutiérrez-Galeano, L. (2026) · ceur-ws.org · doi:10.83095/ceurws_04301_023

Natural Language Processing for Automated Threat Intelligence Extraction

Mohamed, S. Z. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20622475

371Next-Gen Cyber Threat Intelligence: NLPnatural language processing in Action

venket, V. S. K. (2025) · De Gruyter eBooks · doi:10.1515/9783112212158-014