{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T01:25:51Z","timestamp":1782523551926,"version":"3.54.5"},"reference-count":247,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This paper presents a systematic review of research (2020\u20132025) on the role of Large Language Models (LLMs) in cybersecurity, with emphasis on their integration into Big Data infrastructures. Based on a curated corpus of 235 peer-reviewed studies, this review synthesizes evidence across multiple domains to evaluate how models such as GPT-4, BERT, and domain-specific variants support threat detection, incident response, vulnerability assessment, and cyber threat intelligence. The findings confirm that LLMs, particularly when coupled with scalable Big Data pipelines, improve detection accuracy and reduce response latency compared with traditional approaches. However, challenges persist, including adversarial susceptibility, risks of data leakage, computational overhead, and limited transparency. The contribution of this study lies in consolidating fragmented research into a unified taxonomy, identifying sector-specific gaps, and outlining future research priorities: enhancing robustness, mitigating bias, advancing explainability, developing domain-specific models, and optimizing distributed integration. In doing so, this review provides a structured foundation for both academic inquiry and practical adoption of LLM-enabled cyberdefense strategies. Last search: 30 April 2025; methods followed: PRISMA-2020; risk of bias was assessed; random-effects syntheses were conducted.<\/jats:p>","DOI":"10.3390\/info16110957","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T14:02:09Z","timestamp":1762264929000},"page":"957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["LLMs for Cybersecurity in the Big Data Era: A Comprehensive Review of Applications, Challenges, and Future Directions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4632-6511","authenticated-orcid":false,"given":"Aristeidis","family":"Karras","sequence":"first","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0891-6780","authenticated-orcid":false,"given":"Leonidas","family":"Theodorakopoulos","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26334 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4253-7661","authenticated-orcid":false,"given":"Christos","family":"Karras","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6314-7795","authenticated-orcid":false,"given":"Alexandra","family":"Theodoropoulou","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26334 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2039-254X","authenticated-orcid":false,"given":"Ioanna","family":"Kalliampakou","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26334 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2636-3497","authenticated-orcid":false,"given":"Gerasimos","family":"Kalogeratos","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26334 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gelman, H., and Hastings, J.D. (2025). Scalable and Ethical Insider Threat Detection through Data Synthesis and Analysis by LLMs. arXiv.","DOI":"10.1109\/ISDFS65363.2025.11012066"},{"key":"ref_2","unstructured":"Portnoy, A., Azikri, E., and Kels, S. (2024). Towards Automatic Hands-on-Keyboard Attack Detection Using LLMs in EDR Solutions. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Diakhame, M.L., Diallo, C., and Mejri, M. (2024, January 25\u201327). MCM-Llama: A Fine-Tuned Large Language Model for Real-Time Threat Detection through Security Event Correlation. Proceedings of the 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET), Sydney, Australia.","DOI":"10.1109\/ICECET61485.2024.10698464"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"143806","DOI":"10.1109\/ACCESS.2024.3468914","article-title":"Applications of LLMs for Generating Cyber Security Exercise Scenarios","volume":"12","author":"Hashmi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kwan, W.C., Zeng, X., Jiang, Y., Wang, Y., Li, L., Shang, L., Jiang, X., Liu, Q., and Wong, K.F. (2024). Mt-eval: A multi-turn capabilities evaluation benchmark for large language models. arXiv.","DOI":"10.18653\/v1\/2024.emnlp-main.1124"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, H., Wang, S., Li, N., Wang, K., Zhao, Y., Chen, K., Yu, T., Liu, Y., and Wang, H. (2024). Large language models for cyber security: A systematic literature review. arXiv.","DOI":"10.1145\/3769676"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sahoo, P., Singh, A.K., Saha, S., Jain, V., Mondal, S., and Chadha, A. (2024). A systematic survey of prompt engineering in large language models: Techniques and applications. arXiv.","DOI":"10.1007\/979-8-8688-0569-1_4"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104016","DOI":"10.1016\/j.cose.2024.104016","article-title":"A survey of large language models for cyber threat detection","volume":"145","author":"Chen","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_9","unstructured":"Ali, T., and Kostakos, P. (2023). Huntgpt: Integrating machine learning-based anomaly detection and explainable ai with large language models (llms). arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zaboli, A., Choi, S.L., Song, T.J., and Hong, J. (2024, January 21\u201325). Chatgpt and other large language models for cybersecurity of smart grid applications. Proceedings of the 2024 IEEE Power & Energy Society General Meeting (PESGM), Seattle, WA, USA.","DOI":"10.1109\/PESGM51994.2024.10688863"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Omar, M., Zangana, H.M., Al-Karaki, J.N., and Mohammed, D. (2024, January 7\u20139). Harnessing LLMs for IoT Malware Detection: A Comparative Analysis of BERT and GPT-2. Proceedings of the 2024 8th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkiye.","DOI":"10.1109\/ISMSIT63511.2024.10757249"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"G\u00fcven, M. (2024). A Comprehensive Review of Large Language Models in Cyber Security. Int. J. Comput. Exp. Sci. Eng., 10.","DOI":"10.22399\/ijcesen.469"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"57","DOI":"10.30574\/wjaets.2024.13.1.0395","article-title":"Large Language Models (LLMs) for Cybersecurity: A Systematic Review","volume":"13","author":"Gholami","year":"2024","journal-title":"World J. Adv. Eng. Technol. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42400-025-00361-w","article-title":"When llms meet cybersecurity: A systematic literature review","volume":"8","author":"Zhang","year":"2025","journal-title":"Cybersecurity"},{"key":"ref_15","unstructured":"Wan, S., Nikolaidis, C., Song, D., Molnar, D., Crnkovich, J., Grace, J., Bhatt, M., Chennabasappa, S., Whitman, S., and Ding, S. (2024). Cyberseceval 3: Advancing the evaluation of cybersecurity risks and capabilities in large language models. arXiv."},{"key":"ref_16","unstructured":"Bhatt, M., Chennabasappa, S., Li, Y., Nikolaidis, C., Song, D., Wan, S., Ahmad, F., Aschermann, C., Chen, Y., and Kapil, D. (2024). Cyberseceval 2: A wide-ranging cybersecurity evaluation suite for large language models. arXiv."},{"key":"ref_17","unstructured":"Nguyen, T., Nguyen, H., Ijaz, A., Sheikhi, S., Vasilakos, A.V., and Kostakos, P. (2024). Large language models in 6g security: Challenges and opportunities. arXiv."},{"key":"ref_18","first-page":"576","article-title":"Optimizing Healthcare Efficiency with Local Large Language Models","volume":"160","author":"Lorencin","year":"2025","journal-title":"Intell. Hum. Syst. Integr. (IHSI 2025) Integr. People Intell. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nagaraja, N., and Bah\u015fi, H. (2025, January 20\u201322). Cyber Threat Modeling of an LLM-Based Healthcare System. Proceedings of the 11th International Conference on Information Systems Security and Privacy (ICISSP 2025), Porto, Portugal.","DOI":"10.5220\/0013289700003899"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Karras, A., Giannaros, A., Karras, C., Giotopoulos, K.C., Tsolis, D., and Sioutas, S. (2023, January 10\u201312). Edge Artificial Intelligence in Large-Scale IoT Systems, Applications, and Big Data Infrastructures. Proceedings of the 2023 8th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Piraeus, Greece.","DOI":"10.1109\/SEEDA-CECNSM61561.2023.10470756"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Terawi, N., Ashqar, H.I., Darwish, O., Alsobeh, A., Zahariev, P., and Tashtoush, Y. (2025). Enhanced Detection of Intrusion Detection System in Cloud Networks Using Time-Aware and Deep Learning Techniques. Computers, 14.","DOI":"10.3390\/computers14070282"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Schizas, N., Karras, A., Karras, C., and Sioutas, S. (2022). TinyML for ultra-low power AI and large scale IoT deployments: A systematic review. Future Internet, 14.","DOI":"10.3390\/fi14120363"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Harasees, A., Al-Ahmad, B., Alsobeh, A., and Abuhussein, A. (2024, January 24\u201327). A secure IoT framework for remote health monitoring using fog computing. Proceedings of the 2024 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS), Dubrovnik, Croatia.","DOI":"10.1109\/ICCNS62192.2024.10776425"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1504\/IJCC.2024.136279","article-title":"A cloud-based IoT smart water distribution framework utilising BIP component: Jordan as a model","volume":"13","author":"Alshattnawi","year":"2024","journal-title":"Int. J. Cloud Comput."},{"key":"ref_25","first-page":"108","article-title":"Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization","volume":"1","author":"Sharafaldin","year":"2018","journal-title":"ICISSP"},{"key":"ref_26","unstructured":"Order of the Overflow (2025, October 27). DEF CON Capture the Flag 2019 Dataset. Available online: https:\/\/oooverflow.io\/dc-ctf-2019-finals\/."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fontugne, R., Fukuda, K., and Akiba, S. (2010, January 15\u201317). MAWILab: Combining Diverse Anomaly Detectors for Automated Anomaly Labeling and Performance Benchmarking. Proceedings of the Symposium on Recent Advances in Intrusion Detection (RAID), Ottawa, ON, Canada.","DOI":"10.1145\/1921168.1921179"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lippmann, R.P., Haines, J.W., Fried, D.J., Korba, J., and Das, K. (2000, January 25\u201327). The 1999 DARPA Off-Line Intrusion Detection Evaluation. Proceedings of the DARPA Information Survivability Conference and Exposition (DISCEX), Hilton Head, SC, USA.","DOI":"10.1007\/3-540-39945-3_11"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sagiroglu, S., and Sinanc, D. (2013, January 20\u201324). Big data: A review. Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA.","DOI":"10.1109\/CTS.2013.6567202"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S.A., Montesano, N., Tariq, M.I., De-la Hoz-Franco, E., and De-La-Hoz-Valdiris, E. (2022). Trends and future perspective challenges in big data. Advances in Intelligent Data Analysis and Applications, Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, Arad, Romania, 15\u201318 October 2019, Springer.","DOI":"10.1007\/978-981-16-5036-9_30"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.future.2022.01.017","article-title":"A survey on blockchain for big data: Approaches, opportunities, and future directions","volume":"131","author":"Deepa","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Han, X., Gstrein, O.J., and Andrikopoulos, V. (2024). When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data. Front. Big Data, 7.","DOI":"10.3389\/fdata.2024.1441869"},{"key":"ref_33","first-page":"1","article-title":"Real-time big data analytics for data stream challenges: An overview","volume":"2","author":"Hassan","year":"2022","journal-title":"Eur. J. Inf. Technol. Comput. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/17445760.2014.925548","article-title":"Comprehensive analysis of big data variety landscape","volume":"30","author":"Abawajy","year":"2015","journal-title":"Int. J. Parallel Emergent Distrib. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MCOM.2015.7010514","article-title":"Overcoming the challenge of variety: Big data abstraction, the next evolution of data management for AAL communication systems","volume":"53","author":"Mao","year":"2015","journal-title":"IEEE Commun. Mag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pendyala, V. (2018). Veracity of big data. Machine Learning and Other Approaches to Verifying Truthfulness, Apress.","DOI":"10.1007\/978-1-4842-3633-8"},{"key":"ref_37","unstructured":"Berti-Equille, L., and Borge-Holthoefer, J. (2022). Veracity of Data, Springer Nature."},{"key":"ref_38","first-page":"365","article-title":"Extraction for Big Data Cyber Security Analytics","volume":"Volume 993","author":"Tahseen","year":"2024","journal-title":"Advances in Computational Intelligence and Informatics, Proceedings of ICACII 2023"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Vernik, G., Factor, M., Kolodner, E.K., Ofer, E., Michiardi, P., and Pace, F. (2017, January 24\u201327). Stocator: An object store aware connector for apache spark. Proceedings of the 2017 Symposium on Cloud Computing, Santa Clara, CA, USA.","DOI":"10.1145\/3127479.3134761"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rupprecht, L., Zhang, R., Owen, B., Pietzuch, P., and Hildebrand, D. (2017, January 4\u20137). SwiftAnalytics: Optimizing Object Storage for Big Data Analytics. Proceedings of the 2017 IEEE International Conference on Cloud Engineering (IC2E), Vancouver, BC, Canada.","DOI":"10.1109\/IC2E.2017.19"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Baek, S., and Kim, Y.G. (2021). C4I system security architecture: A perspective on big data lifecycle in a military environment. Sustainability, 13.","DOI":"10.3390\/su132413827"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Al-Kateb, M., Eltabakh, M.Y., Al-Omari, A., and Brown, P.G. (2022, January 9\u201312). Analytics at Scale: Evolution at Infrastructure and Algorithmic Levels. Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICDE53745.2022.00302"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"de Sousa, V.M., and Cura, L.M.d.V. (2018, January 19\u201321). Logical design of graph databases from an entity-relationship conceptual model. Proceedings of the 20th International Conference on Information Integration and Web-Based Applications & Services, Yogyakarta, Indonesia.","DOI":"10.1145\/3282373.3282375"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Thepa, T., Ateetanan, P., Khubpatiwitthayakul, P., and Fugkeaw, S. (2024, January 19\u201322). Design and Development of Scalable SIEM as a Service using Spark and Anomaly Detection. Proceedings of the 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE), Phuket, Thailand.","DOI":"10.1109\/JCSSE61278.2024.10613640"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Alawadhi, R., Aalmohamed, H., Alhashemi, S., and Alkhazaleh, H.A. (2024, January 12\u201314). Application of Big Data in Cybersecurity. Proceedings of the 2024 7th International Conference on Signal Processing and Information Security (ICSPIS), Online.","DOI":"10.1109\/ICSPIS63676.2024.10812589"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.30574\/wjarr.2024.22.2.1575","article-title":"The role of big data in detecting and preventing financial fraud in digital transactions","volume":"22","author":"Udeh","year":"2024","journal-title":"World J. Adv. Res. Rev."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, L., Qiang, F., and Ma, L. (2024, January 26\u201328). Advancing Cybersecurity: Graph Neural Networks in Threat Intelligence Knowledge Graphs. Proceedings of the International Conference on Algorithms, Software Engineering, and Network Security, Nanchang, China.","DOI":"10.1145\/3677182.3677314"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Gulbay, B., and Demirci, M. (2024). A Framework for Developing Strategic Cyber Threat Intelligence from Advanced Persistent Threat Analysis Reports Using Graph-Based Algorithms. Preprints.","DOI":"10.20944\/preprints202407.1408.v1"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"86910","DOI":"10.1109\/ACCESS.2023.3304640","article-title":"Cyberattack graph modeling for visual analytics","volume":"11","author":"Rabzelj","year":"2023","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"45","DOI":"10.54097\/2aw6d483","article-title":"Algorithm Innovation and Integration with Big Data Technology in the Field of Information Security: Current Status and Future Development","volume":"7","author":"Wang","year":"2024","journal-title":"Acad. J. Eng. Technol. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Artioli, P., Maci, A., and Magr\u00ec, A. (2024). A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics. Front. Big Data, 7.","DOI":"10.3389\/fdata.2024.1375818"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, J., Yan, T., An, D., Liang, Z., Guo, C., Hu, H., Luo, Q., Li, H., Wang, H., and Zeng, S. (2021, January 22\u201326). A comprehensive security operation center based on big data analytics and threat intelligence. Proceedings of the International Symposium on Grids & Clouds, Taipei, Taiwan.","DOI":"10.22323\/1.378.0028"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bharani, D., Lakshmi Priya, V., and Saravanan, S. (2024, January 17\u201318). Adaptive Real-Time Malware Detection for IoT Traffic Streams: A Comparative Study of Concept Drift Detection Techniques. Proceedings of the 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), Bengaluru, India.","DOI":"10.1109\/ICICNIS64247.2024.10823179"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"K, S., K S, N., S, P., S P, M. (2024, January 14\u201315). Analysis, Trends, and Utilization of Security Information and Event Management (SIEM) in Critical Infrastructures. Proceedings of the 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS60874.2024.10717237"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Saipranith, S., Singh, A.K., Agrawal, N., and Chilumula, S. (2024, January 24\u201328). SwiftFrame: Developing Low-latency Near Real-time Response Framework. Proceedings of the 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India.","DOI":"10.1109\/ICCCNT61001.2024.10725017"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Polepaka, S., Bansal, S., Al-Fatlawy, R.R., Subburam, S., Lakra, P.P., and Neyyila, S. (2024, January 23\u201324). Cloud-Based Marketing Analytics Using Apache Flink for Real-Time Data Insights. Proceedings of the 2024 International Conference on IoT, Communication and Automation Technology (ICICAT), Gorakhpur, India.","DOI":"10.1109\/ICICAT62666.2024.10923360"},{"key":"ref_57","unstructured":"Kanka, V. (2024). Scaling Big Data: Leveraging LLMs for Enterprise Success, Libertatem Media Private Limited."},{"key":"ref_58","first-page":"36","article-title":"Real-Time AI-Based Threat Intelligence for Cloud Security Enhancement","volume":"3","author":"Nikolai","year":"2025","journal-title":"Innov. Int. Multi-Discip. J. Appl. Technol."},{"key":"ref_59","unstructured":"Chitimoju, S. (2025). Enhancing Cyber Threat Intelligence with NLP and Large Language Models. J. Big Data Smart Syst., 6, Available online: https:\/\/universe-publisher.com\/index.php\/jbds\/article\/view\/80."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Tanksale, V. (2024, January 19\u201322). Cyber Threat Hunting Using Large Language Models. Proceedings of the International Congress on Information and Communication Technology, London, UK.","DOI":"10.1007\/978-981-97-3289-0_50"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wu, Y. (2024). The Role of Mining and Detection of Big Data Processing Techniques in Cybersecurity. Appl. Math. Nonlinear Sci., 9.","DOI":"10.2478\/amns-2024-0942"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"279","DOI":"10.51903\/jtie.v3i3.200","article-title":"The Enhancing Cybersecurity with AI Algorithms and Big Data Analytics: Challenges and Solutions","volume":"3","author":"Nugroho","year":"2024","journal-title":"J. Technol. Inform. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"175","DOI":"10.58496\/MJBD\/2024\/012","article-title":"A framework for automated big data analytics in cybersecurity threat detection","volume":"2024","author":"Ameedeen","year":"2024","journal-title":"Mesopotamian J. Big Data"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"203","DOI":"10.30574\/gscarr.2024.19.3.0211","article-title":"Enhancing cybersecurity protocols in the era of big data and advanced analytics","volume":"19","author":"Nwobodo","year":"2024","journal-title":"GSC Adv. Res. Rev."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"484","DOI":"10.3390\/fintech2030028","article-title":"Big data-driven banking operations: Opportunities, challenges, and data security perspectives","volume":"2","author":"Hasan","year":"2023","journal-title":"FinTech"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Sufi, F., and Alsulami, M. (2025). Mathematical Modeling and Clustering Framework for Cyber Threat Analysis Across Industries. Mathematics, 13.","DOI":"10.3390\/math13040655"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chinta, P.C.R., Jha, K.M., Velaga, V., Moore, C., Routhu, K., and Sadaram, G. (2024). Harnessing Big Data and AI-Driven ERP Systems to Enhance Cybersecurity Resilience in Real-Time Threat Environments. SSRN Electron. J.","DOI":"10.2139\/ssrn.5151788"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Jagadeesan, D., Kartheesan, L., Purushotham, B., Krishna, S.T., Kumar, S.N., and Asha, G. (2024, January 4\u20136). Data Analytics Techniques for Privacy Protection in Cybersecurity for Leveraging Machine Learning for Advanced Threat Detection. Proceedings of the 2024 5th IEEE Global Conference for Advancement in Technology (GCAT), Bangalore, India.","DOI":"10.1109\/GCAT62922.2024.10923914"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Singh, R., Aravindan, V., Mishra, S., and Singh, S.K. (2025, January 6\u201310). Streamlined Data Pipeline for Real-Time Threat Detection and Model Inference. Proceedings of the 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS), Bengaluru, India.","DOI":"10.1109\/COMSNETS63942.2025.10885573"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Dewasiri, N.J., Dharmarathna, D.G., and Choudhary, M. (2024). Leveraging artificial intelligence for enhanced risk management in banking: A systematic literature review. Artificial Intelligence Enabled Management: An Emerging Economy Perspective, Walter de Gruyter GmbH & Co. KG.","DOI":"10.1515\/9783111172408-013"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1108\/IJBM-08-2024-0490","article-title":"Generative AI in banking: Empirical insights on integration, challenges and opportunities in a regulated industry","volume":"43","author":"Moharrak","year":"2025","journal-title":"Int. J. Bank Mark."},{"key":"ref_72","unstructured":"Fernandez, J., Wehrstedt, L., Shamis, L., Elhoushi, M., Saladi, K., Bisk, Y., Strubell, E., and Kahn, J. (2024). Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training. arXiv."},{"key":"ref_73","unstructured":"Tang, Z., Kang, X., Yin, Y., Pan, X., Wang, Y., He, X., Wang, Q., Zeng, R., Zhao, K., and Shi, S. (2024). Fusionllm: A decentralized llm training system on geo-distributed gpus with adaptive compression. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yang, F., Peng, S., Sun, N., Wang, F., Wang, Y., Wu, F., Qiu, J., and Pan, A. (2024, January 12\u201315). Holmes: Towards distributed training across clusters with heterogeneous NIC environment. Proceedings of the 53rd International Conference on Parallel Processing, Gotland, Sweden.","DOI":"10.1145\/3673038.3673095"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Chen, Z., Shao, H., Li, Y., Lu, H., and Jin, J. (2022, January 4\u20135). Policy-Based Access Control System for Delta Lake. Proceedings of the 2022 Tenth International Conference on Advanced Cloud and Big Data (CBD), Guilin, China.","DOI":"10.1109\/CBD58033.2022.00020"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Tang, S., He, B., Yu, C., Li, Y., and Li, K. (2023, January 3\u20137). A Survey on Spark Ecosystem: Big Data Processing Infrastructure, Machine Learning, and Applications (Extended abstract). Proceedings of the 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, CA, USA.","DOI":"10.1109\/ICDE55515.2023.00316"},{"key":"ref_77","unstructured":"Douillard, A., Feng, Q., Rusu, A.A., Chhaparia, R., Donchev, Y., Kuncoro, A., Ranzato, M., Szlam, A., and Shen, J. (2023). Diloco: Distributed low-communication training of language models. arXiv."},{"key":"ref_78","first-page":"42330","article-title":"Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls","volume":"36","author":"Li","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"ofaf021","DOI":"10.1093\/ofid\/ofaf021","article-title":"Coronavirus Disease 2019 (COVID-19) Real World Data Infrastructure: A Big-Data Resource for Study of the Impact of COVID-19 in Patient Populations With Immunocompromising Conditions","volume":"12","author":"Crawford","year":"2025","journal-title":"Open Forum Infect. Dis."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Levandoski, J., Casto, G., Deng, M., Desai, R., Edara, P., Hottelier, T., Hormati, A., Johnson, A., Johnson, J., and Kurzyniec, D. (2024, January 9\u201315). BigLake: BigQuery\u2019s Evolution toward a Multi-Cloud Lakehouse. Proceedings of the Companion of the 2024 International Conference on Management of Data, Santiago, Chile.","DOI":"10.1145\/3626246.3653388"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Stankov, I., and Dulgerov, E. (2024, January 21\u201322). Comparing Azure Sentinel and ML-Extended Solutions Applied to a Zero Trust Architecture. Proceedings of the 2024 32nd National Conference with International Participation (TELECOM), Sofia, Bulgaria.","DOI":"10.1109\/TELECOM63374.2024.10812246"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Mori\u0107, Z., Daki\u0107, V., Kapulica, A., and Regvart, D. (2024). Forensic Investigation Capabilities of Microsoft Azure: A Comprehensive Analysis and Its Significance in Advancing Cloud Cyber Forensics. Electronics, 13.","DOI":"10.3390\/electronics13224546"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"549","DOI":"10.48175\/IJARSCT-18863","article-title":"Securing Cloud Infrastructure: An In-Depth Analysis of Microsoft Azure Security","volume":"4","author":"Borra","year":"2024","journal-title":"Int. J. Adv. Res. Sci. Commun. Technol. (IJARSCT)"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Tuyishime, E., Balan, T.C., Cotfas, P.A., Cotfas, D.T., and Rekeraho, A. (2023). Enhancing cloud security\u2014Proactive threat monitoring and detection using a siem-based approach. Appl. Sci., 13.","DOI":"10.3390\/app132212359"},{"key":"ref_85","unstructured":"Shah, S., and Parast, F.K. (2024). AI-Driven Cyber Threat Intelligence Automation. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"103645","DOI":"10.1016\/j.adhoc.2024.103645","article-title":"Pllm-cs: Pre-trained large language model (llm) for cyber threat detection in satellite networks","volume":"166","author":"Hassanin","year":"2025","journal-title":"Ad Hoc Netw."},{"key":"ref_87","unstructured":"Jing, P., Tang, M., Shi, X., Zheng, X., Nie, S., Wu, S., Yang, Y., and Luo, X. (2024). SecBench: A Comprehensive Multi-Dimensional Benchmarking Dataset for LLMs in Cybersecurity. arXiv."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Marantos, C., Evangelatos, S., Veroni, E., Lalas, G., Chasapas, K., Christou, I.T., and Lappas, P. (2024, January 15\u201318). Leveraging Large Language Models for Dynamic Scenario Building targeting Enhanced Cyber-threat Detection and Security Training. Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA.","DOI":"10.1109\/BigData62323.2024.10825681"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.iotcps.2025.01.001","article-title":"Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities","volume":"5","author":"Ferrag","year":"2025","journal-title":"Internet Things Cyber-Phys. Syst."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Kasri, W., Himeur, Y., Alkhazaleh, H.A., Tarapiah, S., Atalla, S., Mansoor, W., and Al-Ahmad, H. (2025). From Vulnerability to Defense: The Role of Large Language Models in Enhancing Cybersecurity. Computation, 13.","DOI":"10.3390\/computation13020030"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"e76869","DOI":"10.34117\/bjdv11n1-062","article-title":"Navigating the dual-edged sword of generative AI in cybersecurity","volume":"11","year":"2025","journal-title":"Braz. J. Dev."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Motlagh, F.N., Hajizadeh, M., Majd, M., Najafi, P., Cheng, F., and Meinel, C. (2024). Large language models in cybersecurity: State-of-the-art. arXiv.","DOI":"10.5220\/0013377600003899"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Pan, Z., Liu, J., Dai, Y., and Fan, W. (2024, January 23\u201325). Large Language Model-enabled Vulnerability Investigation: A Review. Proceedings of the 2024 International Conference on Intelligent Computing and Next Generation Networks (ICNGN), Bangkok, Thailand.","DOI":"10.1109\/ICNGN63705.2024.10871716"},{"key":"ref_94","unstructured":"Bai, G., Chai, Z., Ling, C., Wang, S., Lu, J., Zhang, N., Shi, T., Yu, Z., Zhu, M., and Zhang, Y. (2024). Beyond efficiency: A systematic survey of resource-efficient large language models. arXiv."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3736750","article-title":"Resource-efficient algorithms and systems of foundation models: A survey","volume":"57","author":"Xu","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_96","unstructured":"Liu, J., Liao, Y., Xu, H., and Xu, Y. (2025). Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Theodorakopoulos, L., Karras, A., Theodoropoulou, A., and Kampiotis, G. (2024). Benchmarking Big Data Systems: Performance and Decision-Making Implications in Emerging Technologies. Technologies, 12.","DOI":"10.3390\/technologies12110217"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Theodorakopoulos, L., Karras, A., and Krimpas, G.A. (2025). Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics. Algorithms, 18.","DOI":"10.3390\/a18020074"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Karras, C., Theodorakopoulos, L., Karras, A., and Krimpas, G.A. (2024). Efficient algorithms for range mode queries in the big data era. Information, 15.","DOI":"10.3390\/info15080450"},{"key":"ref_100","unstructured":"Lin, Z., Cui, J., Liao, X., and Wang, X. (2024, January 14\u201316). Malla: Demystifying real-world large language model integrated malicious services. Proceedings of the 33rd USENIX Security Symposium (USENIX Security 24), Philadelphia, PA, USA."},{"key":"ref_101","unstructured":"Charan, P., Chunduri, H., Anand, P.M., and Shukla, S.K. (2023). From text to mitre techniques: Exploring the malicious use of large language models for generating cyber attack payloads. arXiv."},{"key":"ref_102","unstructured":"Clairoux-Trepanier, V., Beauchamp, I.M., Ruellan, E., Paquet-Clouston, M., Paquette, S.O., and Clay, E. (2024). The use of large language models (llm) for cyber threat intelligence (cti) in cybercrime forums. arXiv."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Majumdar, D., Arjun, S., Boyina, P., Rayidi, S.S.P., Sai, Y.R., and Gangashetty, S.V. (2024, January 3\u20134). Beyond text: Nefarious actors harnessing llms for strategic advantage. Proceedings of the 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), Gurugram, India.","DOI":"10.1109\/ISCS61804.2024.10581181"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Zhao, S., Jia, M., Tuan, L.A., Pan, F., and Wen, J. (2024). Universal vulnerabilities in large language models: Backdoor attacks for in-context learning. arXiv.","DOI":"10.18653\/v1\/2024.emnlp-main.642"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Ni, T., Lee, W.B., and Zhao, Q. (2025). A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations. arXiv.","DOI":"10.53941\/tai.2025.100003"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/MNET.2024.3367788","article-title":"A comprehensive overview of backdoor attacks in large language models within communication networks","volume":"38","author":"Yang","year":"2024","journal-title":"IEEE Netw."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Ge, H., Li, Y., Wang, Q., Zhang, Y., and Tang, R. (2024). When Backdoors Speak: Understanding LLM Backdoor Attacks Through Model-Generated Explanations. arXiv.","DOI":"10.18653\/v1\/2025.acl-long.114"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Li, Y., Xu, Z., Jiang, F., Niu, L., Sahabandu, D., Ramasubramanian, B., and Poovendran, R. (2024). Cleangen: Mitigating backdoor attacks for generation tasks in large language models. arXiv.","DOI":"10.18653\/v1\/2024.emnlp-main.514"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"367","DOI":"10.3390\/make6010018","article-title":"Prompt engineering or fine-tuning? A case study on phishing detection with large language models","volume":"6","author":"Trad","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_110","first-page":"21","article-title":"Harnessing large language models to simulate realistic human responses to social engineering attacks: A case study","volume":"6","author":"Asfour","year":"2023","journal-title":"Int. J. Cybersecur. Intell. Cybercrime"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Roy, S.S., Thota, P., Naragam, K.V., and Nilizadeh, S. (2024, January 19\u201323). From chatbots to phishbots?: Phishing scam generation in commercial large language models. Proceedings of the 2024 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA.","DOI":"10.1109\/SP54263.2024.00182"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Ai, L., Kumarage, T., Bhattacharjee, A., Liu, Z., Hui, Z., Davinroy, M., Cook, J., Cassani, L., Trapeznikov, K., and Kirchner, M. (2024). Defending against social engineering attacks in the age of llms. arXiv.","DOI":"10.18653\/v1\/2024.emnlp-main.716"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"e402","DOI":"10.1002\/spy2.402","article-title":"An improved transformer-based model for detecting phishing, spam and ham emails: A large language model approach","volume":"7","author":"Jamal","year":"2024","journal-title":"Secur. Priv."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Malloy, T., Ferreira, M.J., Fang, F., and Gonzalez, C. (2025). Training Users Against Human and GPT-4 Generated Social Engineering Attacks. arXiv.","DOI":"10.1007\/978-3-031-92833-8_4"},{"key":"ref_115","unstructured":"Wan, Z., Wang, X., Liu, C., Alam, S., Zheng, Y., Liu, J., Qu, Z., Yan, S., Zhu, Y., and Zhang, Q. (2023). Efficient large language models: A survey. arXiv."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Afane, K., Wei, W., Mao, Y., Farooq, J., and Chen, J. (2024, January 15\u201318). Next-Generation Phishing: How LLM Agents Empower Cyber Attackers. Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA.","DOI":"10.1109\/BigData62323.2024.10825018"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Kulkarni, A., Balachandran, V., Divakaran, D.M., and Das, T. (2024). From ml to llm: Evaluating the robustness of phishing webpage detection models against adversarial attacks. arXiv.","DOI":"10.1145\/3737295"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Kamruzzaman, A.S., Thakur, K., and Mahbub, S. (2024, January 1\u20132). AI Tools Building Cybercrime & Defenses. Proceedings of the 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), Victoria, Seychelles.","DOI":"10.1109\/ACDSA59508.2024.10467401"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Yang, S., Zhu, S., Wu, Z., Wang, K., Yao, J., Wu, J., Hu, L., Li, M., Wong, D.F., and Wang, D. (2025). Fraud-R1: A Multi-Round Benchmark for Assessing the Robustness of LLM Against Augmented Fraud and Phishing Inducements. arXiv.","DOI":"10.18653\/v1\/2025.findings-acl.226"},{"key":"ref_120","unstructured":"Wang, J., Huang, Z., Liu, H., Yang, N., and Xiao, Y. (2023). Defecthunter: A novel llm-driven boosted-conformer-based code vulnerability detection mechanism. arXiv."},{"key":"ref_121","unstructured":"Andriushchenko, M., Souly, A., Dziemian, M., Duenas, D., Lin, M., Wang, J., Hendrycks, D., Zou, A., Kolter, Z., and Fredrikson, M. (2024). Agentharm: A benchmark for measuring harmfulness of llm agents. arXiv."},{"key":"ref_122","unstructured":"Jiang, L. (2024). Detecting scams using large language models. arXiv."},{"key":"ref_123","unstructured":"Hays, S., and White, J. (2024). Employing llms for incident response planning and review. arXiv."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"655","DOI":"10.56038\/oprd.v5i1.616","article-title":"AI-Enhanced Cybersecurity Vulnerability-Based Prevention, Defense, and Mitigation using Generative AI","volume":"5","year":"2024","journal-title":"Orclever Proc. Res. Dev."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"106066","DOI":"10.1016\/j.clsr.2024.106066","article-title":"Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity","volume":"55","author":"Novelli","year":"2024","journal-title":"Comput. Law Secur. Rev."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Derasari, P., and Venkataramani, G. (2024, January 12\u201314). EPIC: Efficient and Proactive Instruction-level Cyberdefense. Proceedings of the Great Lakes Symposium on VLSI 2024, GLSVLSI \u201924, Clearwater, FL, USA.","DOI":"10.1145\/3649476.3658749"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Bataineh, A.Q., Abu-AlSondos, I.A., Idris, M., Mushtaha, A.S., and Qasim, D.M. (2023, January 5\u20136). The role of big data analytics in driving innovation in digital marketing. Proceedings of the 2023 9th International Conference on Optimization and Applications (ICOA), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICOA58279.2023.10308854"},{"key":"ref_128","first-page":"1099","article-title":"Big data academic and learning analytics: Connecting the dots for academic excellence in higher education","volume":"32","author":"Chaurasia","year":"2018","journal-title":"Int. J. Educ. Manag."},{"key":"ref_129","unstructured":"Hassanin, M., and Moustafa, N. (2024). A comprehensive overview of large language models (llms) for cyber defences: Opportunities and directions. arXiv."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Ji, H., Yang, J., Chai, L., Wei, C., Yang, L., Duan, Y., Wang, Y., Sun, T., Guo, H., and Li, T. (2024). Sevenllm: Benchmarking, eliciting, and enhancing abilities of large language models in cyber threat intelligence. arXiv.","DOI":"10.18653\/v1\/2024.findings-acl.878"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Bokkena, B. (2024, January 18\u201320). Enhancing IT Security with LLM-Powered Predictive Threat Intelligence. Proceedings of the 2024 5th International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India.","DOI":"10.1109\/ICOSEC61587.2024.10722712"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Balasubramanian, P., Ali, T., Salmani, M., KhoshKholgh, D., and Kostakos, P. (2024, January 15\u201318). Hex2Sign: Automatic IDS Signature Generation from Hexadecimal Data using LLMs. Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA.","DOI":"10.1109\/BigData62323.2024.10825710"},{"key":"ref_133","unstructured":"Webb, B.K., Purohit, S., and Meyur, R. (2024). Cyber knowledge completion using large language models. arXiv."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Song, J., Wang, X., Zhu, J., Wu, Y., Cheng, X., Zhong, R., and Niu, C. (2024, January 12\u201316). RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, Miami, FL, USA.","DOI":"10.18653\/v1\/2024.emnlp-industry.113"},{"key":"ref_135","unstructured":"Gandhi, P.A., Wudali, P.N., Amaru, Y., Elovici, Y., and Shabtai, A. (2025). SHIELD: APT Detection and Intelligent Explanation Using LLM. arXiv."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Ji, Z., Chen, D., Ishii, E., Cahyawijaya, S., Bang, Y., Wilie, B., and Fung, P. (2024). Llm internal states reveal hallucination risk faced with a query. arXiv.","DOI":"10.18653\/v1\/2024.blackboxnlp-1.6"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Maity, S., and Arora, J. (2024, January 21\u201323). The Colossal Defense: Security Challenges of Large Language Models. Proceedings of the 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), New Delhi, India.","DOI":"10.1109\/DELCON64804.2024.10866433"},{"key":"ref_138","unstructured":"Ayzenshteyn, D., Weiss, R., and Mirsky, Y. (2024). The Best Defense is a Good Offense: Countering LLM-Powered Cyberattacks. arXiv."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"8658","DOI":"10.1109\/TIFS.2024.3434647","article-title":"Human-in-the-loop cyber intrusion detection using active learning","volume":"19","author":"Kim","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_140","unstructured":"Ghanem, M.C. (2024, January 29\u201331). Advancing IoT and Cloud Security through LLMs, Federated Learning, and Reinforcement Learning. Proceedings of the 7th IEEE Conference on Cloud and Internet of Things (CIoT 2024)\u2014Keynote, Montreal, QC, Canada."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Haryanto, C.Y., Elvira, A.M., Nguyen, T.D., Vu, M.H., Hartanto, Y., Lomempow, E., and Arakala, A. (2024, January 2\u20134). Contextualized AI for Cyber Defense: An Automated Survey using LLMs. Proceedings of the 2024 17th International Conference on Security of Information and Networks (SIN), Sydney, Australia.","DOI":"10.1109\/SIN63213.2024.10871242"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"V, S., P, L.S., P, N.K., V, L.P., and CH, B.S. (2024, January 14\u201315). Data Leakage Detection and Prevention Using Ciphertext-Policy Attribute Based Encryption Algorithm. Proceedings of the 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India.","DOI":"10.1109\/ICRITO61523.2024.10522194"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"e70069","DOI":"10.1002\/ett.70069","article-title":"An Efficient Cyber Security Attack Detection With Encryption Using Capsule Convolutional Polymorphic Graph Attention","volume":"36","author":"Kumar","year":"2025","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Chen, Z. (2021, January 28\u201330). Preventive Measures of Influencing Factors of Computer Network Security Technology. Proceedings of the 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China.","DOI":"10.1109\/ICAICA52286.2021.9498242"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Sarkorn, T., and Chimmanee, K. (2024, January 14\u201315). Review on Zero Trust Architecture Apply In Enterprise Next Generation Firewall. Proceedings of the 2024 8th International Conference on Information Technology (InCIT), Chonburi, Thailand.","DOI":"10.1109\/InCIT63192.2024.10810611"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Mustafa, H.M., Basumallik, S., Vellaithurai, C., and Srivastava, A. (2024, January 13). Threat Detection in Power Grid OT Networks: Unsupervised ML and Cyber Intelligence Sharing with STIX. Proceedings of the 2024 12th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES), Hong Kong, China.","DOI":"10.1109\/MSCPES62135.2024.10542775"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Steingartner, W., Galinec, D., and Zebi\u0107, V. (2024, January 13\u201315). Challenges of Application Programming Interfaces Security: A Conceptual Model in the Changing Cyber Defense Environment and Zero Trust Architecture. Proceedings of the 2024 IEEE 17th International Scientific Conference on Informatics (Informatics), Poprad, Slovakia.","DOI":"10.1109\/Informatics62280.2024.10900929"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"4159","DOI":"10.30574\/ijsra.2024.13.2.2583","article-title":"Zero trust architecture and AI: A synergistic approach to next-generation cybersecurity frameworks","volume":"13","author":"Mmaduekwe","year":"2024","journal-title":"Int. J. Sci. Res. Arch."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Freitas, S., Kalajdjieski, J., Gharib, A., and McCann, R. (2024). AI-Driven Guided Response for Security Operation Centers with Microsoft Copilot for Security. arXiv.","DOI":"10.1145\/3701716.3715209"},{"key":"ref_150","unstructured":"Bono, J., and Xu, A. (2024). Randomized controlled trials for Security Copilot for IT administrators. arXiv."},{"key":"ref_151","unstructured":"Paul, S., Alemi, F., and Macwan, R. (2025). LLM-Assisted Proactive Threat Intelligence for Automated Reasoning. arXiv."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"102976","DOI":"10.1016\/j.telpol.2025.102976","article-title":"Transforming cybersecurity with agentic AI to combat emerging cyber threats","volume":"49","author":"Kshetri","year":"2025","journal-title":"Telecommun. Policy"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Schesny, M., Lutz, N., J\u00e4gle, T., Gerschner, F., Klaiber, M., and Theissler, A. (2024, January 2\u20134). Enhancing Website Fraud Detection: A ChatGPT-Based Approach to Phishing Detection. Proceedings of the 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Osaka, Japan.","DOI":"10.1109\/COMPSAC61105.2024.00205"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Razavi, H., and Jamali, M.R. (2024, January 9\u201310). Large Language Models (LLM) for Estimating the Cost of Cyber-attacks. Proceedings of the 2024 11th International Symposium on Telecommunications (IST), Tehran, Iran.","DOI":"10.1109\/IST64061.2024.10843617"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.30574\/wjarr.2024.22.1.1305","article-title":"AI Cyber Defense and eBPF","volume":"22","author":"Mathew","year":"2024","journal-title":"World J. Adv. Res. Rev."},{"key":"ref_156","unstructured":"Baldoni, R., De Nicola, R., and Prinetto, P. (2018). The Future of Cybersecurity in Italy: Strategic Focus Areas. Projects and Actions to Better Defend Our Country from Cyber Attacks, CINI\u2014Consorzio Interuniversitario Nazionale per l\u2019Informatica. English Edition; Translated from the Italian Volume (Jan 2018, ISBN 9788894137330); last update 20 June 2018."},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Truong, T.C., Diep, Q.B., and Zelinka, I. (2020). Artificial intelligence in the cyber domain: Offense and defense. Symmetry, 12.","DOI":"10.3390\/sym12030410"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"23733","DOI":"10.1109\/ACCESS.2024.3363469","article-title":"Revolutionizing cyber threat detection with large language models: A privacy-preserving bert-based lightweight model for iot\/iiot devices","volume":"12","author":"Ferrag","year":"2024","journal-title":"IEEE Access"},{"key":"ref_159","unstructured":"Metta, S., Chang, I., Parker, J., Roman, M.P., and Ehuan, A.F. (2024). Generative AI in cybersecurity. arXiv."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Benabderrahmane, S., Valtchev, P., Cheney, J., and Rahwan, T. (2025). APT-LLM: Embedding-Based Anomaly Detection of Cyber Advanced Persistent Threats Using Large Language Models. arXiv.","DOI":"10.1109\/ISDFS65363.2025.11011912"},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, Q., Tan, Y., Guo, Z., Zhang, L., and Cui, Y. (2025). Large Language Models powered Network Attack Detection: Architecture, Opportunities and Case Study. arXiv.","DOI":"10.1109\/MNET.2025.3583088"},{"key":"ref_162","unstructured":"Zuo, F., Rhee, J., and Choe, Y.R. (2025). Knowledge Transfer from LLMs to Provenance Analysis: A Semantic-Augmented Method for APT Detection. arXiv."},{"key":"ref_163","unstructured":"Ferrag, M.A., Ndhlovu, M., Tihanyi, N., Cordeiro, L.C., Debbah, M., and Lestable, T. (2023). Revolutionizing cyber threat detection with large language models. arXiv."},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Ren, H., Lan, K., Sun, Z., and Liao, S. (2024, January 27\u201329). CLogLLM: A Large Language Model Enabled Approach to Cybersecurity Log Anomaly Analysis. Proceedings of the 2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC), Wuhan, China.","DOI":"10.1109\/EIECC64539.2024.10929078"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Ismail, I., Kurnia, R., Brata, Z.A., Nelistiani, G.A., Heo, S., Kim, H., and Kim, H. (2025). Toward Robust Security Orchestration and Automated Response in Security Operations Centers with a Hyper-Automation Approach Using Agentic Artificial Intelligence. Information, 16.","DOI":"10.20944\/preprints202502.2134.v1"},{"key":"ref_166","unstructured":"Tallam, K. (2025). CyberSentinel: An Emergent Threat Detection System for AI Security. arXiv."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Kheddar, H. (2024). Transformers and large language models for efficient intrusion detection systems: A comprehensive survey. arXiv.","DOI":"10.1016\/j.inffus.2025.103347"},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Ghimire, A., Ghajari, G., Gurung, K., Sah, L.K., and Amsaad, F. (2025). Enhancing Cybersecurity in Critical Infrastructure with LLM-Assisted Explainable IoT Systems. arXiv.","DOI":"10.1109\/SATC65530.2025.11137104"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Setak, M., and Madani, P. (2024, January 28\u201331). Fine-Tuning LLMs for Code Mutation: A New Era of Cyber Threats. Proceedings of the 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA), Washington, DC, USA.","DOI":"10.1109\/TPS-ISA62245.2024.00043"},{"key":"ref_170","unstructured":"Song, C., Ma, L., Zheng, J., Liao, J., Kuang, H., and Yang, L. (2024). Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection. arXiv."},{"key":"ref_171","unstructured":"Li, Y., Xiang, Z., Bastian, N.D., Song, D., and Li, B. (2024, January 15). IDS-Agent: An LLM Agent for Explainable Intrusion Detection in IoT Networks. Proceedings of the NeurIPS 2024 Workshop on Open-World Agents, Vancouver, BC, Canada."},{"key":"ref_172","unstructured":"Rigaki, M., Catania, C., and Garcia, S. (2024). Hackphyr: A Local Fine-Tuned LLM Agent for Network Security Environments. arXiv."},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Diaf, A., Korba, A.A., Karabadji, N.E., and Ghamri-Doudane, Y. (2024, January 8\u201312). BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction. Proceedings of the GLOBECOM 2024-2024 IEEE Global Communications Conference, Cape Town, South Africa.","DOI":"10.1109\/GLOBECOM52923.2024.10901770"},{"key":"ref_174","unstructured":"Barker, C. (2020). Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware. [Senior Honors Thesis, Liberty University]."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"tyy007","DOI":"10.1093\/cybsec\/tyy007","article-title":"Malware in the future? Forecasting of analyst detection of cyber events","volume":"4","author":"Bakdash","year":"2018","journal-title":"J. Cybersecur."},{"key":"ref_176","unstructured":"Cheng, Y., Bajaber, O., Tsegai, S.A., Song, D., and Gao, P. (2024). CTINEXUS: Leveraging Optimized LLM In-Context Learning for Constructing Cybersecurity Knowledge Graphs Under Data Scarcity. arXiv."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"14029","DOI":"10.1109\/ACCESS.2025.3528114","article-title":"A Comprehensive Review of AI\u2019s Current Impact and Future Prospects in Cybersecurity","volume":"13","author":"Alazab","year":"2025","journal-title":"IEEE Access"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"54","DOI":"10.32996\/jcsts.2024.6.4.8","article-title":"Zero Trust Architecture: Enhancing cybersecurity in enterprise networks","volume":"6","author":"Bashir","year":"2024","journal-title":"J. Comput. Sci. Technol. Stud."},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Hu, X., Chen, H., Bao, H., Wang, W., Liu, F., Zhou, G., and Yin, P. (2024, January 17\u201321). A LLM-based agent for the automatic generation and generalization of IDS rules. Proceedings of the 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Sanya, China.","DOI":"10.1109\/TrustCom63139.2024.00259"},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Bianou, S.G., and Batogna, R.G. (2024, January 2\u20134). PENTEST-AI, an LLM-Powered multi-agents framework for penetration testing automation leveraging mitre attack. Proceedings of the 2024 IEEE International Conference on Cyber Security and Resilience (CSR), London, UK.","DOI":"10.1109\/CSR61664.2024.10679480"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"30907","DOI":"10.1109\/ACCESS.2024.3369906","article-title":"A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods","volume":"12","author":"Alzaabi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Du, D., Guan, X., Liu, Y., Jiang, B., Liu, S., Feng, H., and Liu, J. (November, January 30). MAD-LLM: A Novel Approach for Alert-Based Multi-stage Attack Detection via LLM. Proceedings of the 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), Kaifeng, China.","DOI":"10.1109\/ISPA63168.2024.00279"},{"key":"ref_183","doi-asserted-by":"crossref","unstructured":"Swetha, K., and K, S. (2024, January 21\u201323). Detection of Cybercriminal Activities in Smartphones via NLP-Based Communication Pattern Analysis. Proceedings of the 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), New Delhi, India.","DOI":"10.1109\/DELCON64804.2024.10867101"},{"key":"ref_184","unstructured":"Vieira, A.C., Houmb, S.H., and Insua, D.R. (2014). A graphical adversarial risk analysis model for oil and gas drilling cybersecurity. arXiv."},{"key":"ref_185","unstructured":"Usman, Y., Upadhyay, A., Gyawali, P., and Chataut, R. (2024). Is generative ai the next tactical cyber weapon for threat actors? Unforeseen implications of ai generated cyber attacks. arXiv."},{"key":"ref_186","unstructured":"Wang, L., Wang, J., Jung, K., Thiagarajan, K., Wei, E., Shen, X., Chen, Y., and Li, Z. (2024). From sands to mansions: Enabling automatic full-life-cycle cyberattack construction with llm. arXiv."},{"key":"ref_187","doi-asserted-by":"crossref","unstructured":"Ruhl\u00e4nder, L., Popp, E., Stylidou, M., Khan, S., and Svetinovic, D. (2024, January 19\u201322). On the Security and Privacy Implications of Large Language Models: In-Depth Threat Analysis. Proceedings of the 2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics, Copenhagen, Denmark.","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics62450.2024.00102"},{"key":"ref_188","unstructured":"Gade, P., Lermen, S., Rogers-Smith, C., and Ladish, J. (2023). Badllama: Cheaply removing safety fine-tuning from llama 2-chat 13b. arXiv."},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Josten, M., Schaffeld, M., Lehmann, R., and Weis, T. (2025, January 20\u201322). Navigating the Security Challenges of LLMs: Positioning Target-Side Defenses and Identifying Research Gaps. Proceedings of the 11th International Conference on Information Systems Security and Privacy (ICISSP 2025), Porto, Portugal.","DOI":"10.5220\/0013274700003899"},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s44163-024-00129-0","article-title":"LLM potentiality and awareness: A position paper from the perspective of trustworthy and responsible AI modeling","volume":"4","author":"Sarker","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Pupillo, L., Ferreira, A., and Fantin, S. (2020). Artificial Intelligence and Cybersecurity: Task Force Evaluation of the HLEG Trustworthy AI Assessment List (Pilot Version), Centre for European Policy Studies (CEPS). Ceps Task Force Report.","DOI":"10.2139\/ssrn.3525997"},{"key":"ref_192","doi-asserted-by":"crossref","unstructured":"Shafee, S., Bessani, A., and Ferreira, P.M. (2024). Evaluation of LLM chatbots for OSINT-based cyber threat awareness. arXiv.","DOI":"10.2139\/ssrn.4703135"},{"key":"ref_193","unstructured":"Hariharan, S., Majid, Z.A., Veuthey, J.R., and Haimes, J. (2024). Rethinking CyberSecEval: An LLM-Aided Approach to Evaluation Critique. arXiv."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1111\/jerd.13046","article-title":"Implications of large language models such as ChatGPT for dental medicine","volume":"35","author":"Eggmann","year":"2023","journal-title":"J. Esthet. Restor. Dent."},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Keltek, M., Hu, R., Sani, M.F., and Li, Z. (2024). LSAST\u2013Enhancing Cybersecurity through LLM-supported Static Application Security Testing. arXiv.","DOI":"10.1007\/978-3-031-92882-6_12"},{"key":"ref_196","unstructured":"Tann, W., Liu, Y., Sim, J.H., Seah, C.M., and Chang, E.C. (2023). Using large language models for cybersecurity capture-the-flag challenges and certification questions. arXiv."},{"key":"ref_197","unstructured":"Zhang, Y., Cai, Y., Zuo, X., Luan, X., Wang, K., Hou, Z., Zhang, Y., Wei, Z., Sun, M., and Sun, J. (2024). The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap. arXiv."},{"key":"ref_198","doi-asserted-by":"crossref","unstructured":"Zhao, X., Leng, X., Wang, L., Wang, N., and Liu, Y. (2025). Efficient anomaly detection in tabular cybersecurity data using large language models. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-88050-z"},{"key":"ref_199","first-page":"1","article-title":"General data protection regulation","volume":"25","author":"Regulation","year":"2018","journal-title":"Intouch"},{"key":"ref_200","unstructured":"European Union Artificial Intelligence Act (2024). Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 on Artificial Intelligence, Official Journal of the European Union: L 188, 12 July 2024."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"105336","DOI":"10.1016\/j.clsr.2019.06.007","article-title":"The new EU cybersecurity framework: The NIS Directive, ENISA\u2019s role and the General Data Protection Regulation","volume":"35","author":"Markopoulou","year":"2019","journal-title":"Comput. Law Secur. Rev."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"tyad009","DOI":"10.1093\/cybsec\/tyad009","article-title":"Defining the reporting threshold for a cybersecurity incident under the NIS Directive and the NIS 2 Directive","volume":"9","year":"2023","journal-title":"J. Cybersecur."},{"key":"ref_203","unstructured":"Danezis, G., Domingo-Ferrer, J., Hansen, M., Hoepman, J.H., Metayer, D.L., Tirtea, R., and Schiffner, S. (2015). Privacy and data protection by design-from policy to engineering. arXiv."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"28335","DOI":"10.1109\/ACCESS.2025.3540075","article-title":"Benchmarking and Evaluating Large Language Models in Phishing Detection for Small and Midsize Enterprises: A Comprehensive Analysis","volume":"13","author":"Zhang","year":"2025","journal-title":"IEEE Access"},{"key":"ref_205","unstructured":"Yigit, Y., Buchanan, W.J., Tehrani, M.G., and Maglaras, L. (2024). Review of generative ai methods in cybersecurity. arXiv."},{"key":"ref_206","doi-asserted-by":"crossref","unstructured":"Adamec, M., and Tur\u010dan\u00edk, M. (2024, January 16\u201318). Development of Malware Using Large Language Models. Proceedings of the 2024 New Trends in Signal Processing (NTSP), Demanovska Dolina, Slovakia.","DOI":"10.23919\/NTSP61680.2024.10726304"},{"key":"ref_207","unstructured":"Wahr\u00e9us, J., Hussain, A.M., and Papadimitratos, P. (2025). CySecBench: Generative AI-based CyberSecurity-focused Prompt Dataset for Benchmarking Large Language Models. arXiv."},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Jones, N., Whaiduzzaman, M., Jan, T., Adel, A., Alazab, A., and Alkreisat, A. (2025). A CIA Triad-Based Taxonomy of Prompt Attacks on Large Language Models. Future Internet, 17.","DOI":"10.3390\/fi17030113"},{"key":"ref_209","doi-asserted-by":"crossref","unstructured":"Bhusal, D., Alam, M.T., Nguyen, L., Mahara, A., Lightcap, Z., Frazier, R., Fieblinger, R., Torales, G.L., Blakely, B.A., and Rastogi, N. (2024). SECURE: Benchmarking Large Language Models for Cybersecurity. arXiv.","DOI":"10.1109\/ACSAC63791.2024.00019"},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Guo, Y., Patsakis, C., Hu, Q., Tang, Q., and Casino, F. (2024, January 22\u201324). Outside the comfort zone: Analysing llm capabilities in software vulnerability detection. Proceedings of the European Symposium on Research in Computer Security, Bydgoszcz, Poland.","DOI":"10.1007\/978-3-031-70879-4_14"},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"176751","DOI":"10.1109\/ACCESS.2024.3505983","article-title":"Application of Large Language Models in Cybersecurity: A Systematic Literature Review","volume":"12","author":"Hasanov","year":"2024","journal-title":"IEEE Access"},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Balogh, \u0160., Mlyn\u010dek, M., Vra\u0148\u00e1k, O., and Zajac, P. (2024). Using Generative AI Models to Support Cybersecurity Analysts. Electronics, 13.","DOI":"10.3390\/electronics13234718"},{"key":"ref_213","first-page":"23164","article-title":"CyberQ: Generating Questions and Answers for Cybersecurity Education Using Knowledge Graph-Augmented LLMs","volume":"38","author":"Agrawal","year":"2024","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_214","unstructured":"Nelson, C., Doup\u00e9, A., and Shoshitaishvili, Y. (March, January 26). SENSAI: Large Language Models as Applied Cybersecurity Tutors. Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1, Pittsburgh, PA, USA."},{"key":"ref_215","unstructured":"Yan, Y., Zhang, Y., and Huang, K. (2024). Depending on yourself when you should: Mentoring llm with rl agents to become the master in cybersecurity games. arXiv."},{"key":"ref_216","unstructured":"Tshimula, J.M., Ndona, X., Nkashama, D.K., Tardif, P.M., Kabanza, F., Frappier, M., and Wang, S. (2024). Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective. arXiv."},{"key":"ref_217","doi-asserted-by":"crossref","unstructured":"Lodge, B. (2024, January 15\u201318). RAGe Against the Machine with BERT for Proactive Cybersecurity Posture. Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA.","DOI":"10.1109\/BigData62323.2024.10825122"},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"082","DOI":"10.30574\/ijsra.2020.1.1.0020","article-title":"Integrating Natural Language Processing with Cybersecurity Protocols: Real-Time Analysis of Malicious Intent in Social Engineering Attack","volume":"1","author":"Kakolu","year":"2020","journal-title":"Int. J. Sci. Res. Arch."},{"key":"ref_219","doi-asserted-by":"crossref","unstructured":"Rahman, M.R., Wroblewski, B., Tamanna, M., Rahman, I., Anufryienak, A., and Williams, L. (2024, January 9\u201313). Towards a taxonomy of challenges in security control implementation. Proceedings of the 2024 Annual Computer Security Applications Conference (ACSAC), Honolulu, HI, USA.","DOI":"10.1109\/ACSAC63791.2024.00022"},{"key":"ref_220","unstructured":"Liu, Z. (2024). Multi-Agent Collaboration in Incident Response with Large Language Models. arXiv."},{"key":"ref_221","doi-asserted-by":"crossref","unstructured":"Svoboda, I., and Lande, D. (2024). Enhancing multi-criteria decision analysis with AI: Integrating analytic hierarchy process and GPT-4 for automated decision support. arXiv.","DOI":"10.2139\/ssrn.5069656"},{"key":"ref_222","doi-asserted-by":"crossref","unstructured":"Ou, L., Ni, X., Wu, W., and Tian, Z. (2024, January 23\u201326). CyGPT: Knowledge Graph-Based Enhancement Techniques for Large Language Models in Cybersecurity. Proceedings of the 2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC), Jinan, China.","DOI":"10.1109\/DSC63484.2024.00036"},{"key":"ref_223","doi-asserted-by":"crossref","unstructured":"Kumar, N.M., Lisa, F.T., and Islam, S.R. (2024, January 15\u201318). Prompt Chaining-Assisted Malware Detection: A Hybrid Approach Utilizing Fine-Tuned LLMs and Domain Knowledge-Enriched Cybersecurity Knowledge Graphs. Proceedings of the 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA.","DOI":"10.1109\/BigData62323.2024.10825154"},{"key":"ref_224","unstructured":"Luko\u0161i\u016bt\u0117, K., and Swanda, A. (2025). LLM Cyber Evaluations Don\u2019t Capture Real-World Risk. arXiv."},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"109470","DOI":"10.1109\/ACCESS.2024.3439363","article-title":"Enhancing Autonomous System Security and Resilience With Generative AI: A Comprehensive Survey","volume":"12","author":"Andreoni","year":"2024","journal-title":"IEEE Access"},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Ismail, M., and Alrabaee, S. (2024, January 13\u201316). Empowering Future Cyber Defenders: Advancing Cybersecurity Education in Engineering and Computing with Experiential Learning. Proceedings of the 2024 IEEE Frontiers in Education Conference (FIE), Washington, DC, USA.","DOI":"10.1109\/FIE61694.2024.10892990"},{"key":"ref_227","doi-asserted-by":"crossref","unstructured":"Greco, D., and Chianese, L. (2024, January 11\u201313). Exploiting LLMs for E-Learning: A Cybersecurity Perspective on AI-Generated Tools in Education. Proceedings of the 2024 IEEE International Workshop on Technologies for Defense and Security (TechDefense), Naples, Italy.","DOI":"10.1109\/TechDefense63521.2024.10863662"},{"key":"ref_228","unstructured":"Yu, Y.C., Chiang, T.H., Tsai, C.W., Huang, C.M., and Tsao, W.K. (2025). Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training. arXiv."},{"key":"ref_229","doi-asserted-by":"crossref","unstructured":"Balasubramanian, P., Seby, J., and Kostakos, P. (2023, January 15\u201318). Transformer-based llms in cybersecurity: An in-depth study on log anomaly detection and conversational defense mechanisms. Proceedings of the 2023 IEEE International Conference on Big Data (BigData), Sorrento, Italy.","DOI":"10.1109\/BigData59044.2023.10386976"},{"key":"ref_230","doi-asserted-by":"crossref","unstructured":"Hamid, R., and Brohi, S. (2024). A Review of Large Language Models in Healthcare: Taxonomy, Threats, Vulnerabilities, and Framework. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8110161"},{"key":"ref_231","doi-asserted-by":"crossref","unstructured":"Imtiaz, A., Shehzad, D., Nasim, F., Afzaal, M., Rehman, M., and Imran, A. (2023, January 21\u201324). Analysis of Cybersecurity Measures for Detection, Prevention, and Misbehaviour of Social Systems. Proceedings of the 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/SNAMS60348.2023.10375405"},{"key":"ref_232","doi-asserted-by":"crossref","unstructured":"Yang, X., Pan, L., Zhao, X., Chen, H., Petzold, L., Wang, W.Y., and Cheng, W. (2023). A survey on detection of llms-generated content. arXiv.","DOI":"10.18653\/v1\/2024.findings-emnlp.572"},{"key":"ref_233","doi-asserted-by":"crossref","unstructured":"Nana, S.R., Bassol\u00e9, D., Guel, D., and Si\u00e9, O. (2024). Deep Learning and Web Applications Vulnerabilities Detection: An Approach Based on Large Language Models. Int. J. Adv. Comput. Sci. Appl., 15.","DOI":"10.14569\/IJACSA.2024.01507135"},{"key":"ref_234","doi-asserted-by":"crossref","unstructured":"Cao, D., Liao, Y., and Shang, X. (2024). RealVul: Can We Detect Vulnerabilities in Web Applications with LLM?. arXiv.","DOI":"10.18653\/v1\/2024.emnlp-main.472"},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1109\/TSE.2025.3548168","article-title":"SecureFalcon: Are we there yet in automated software vulnerability detection with LLMs?","volume":"51","author":"Ferrag","year":"2025","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"493","DOI":"10.3390\/jcp3030025","article-title":"Autonomous vehicles: Sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions","volume":"3","author":"Giannaros","year":"2023","journal-title":"J. Cybersecur. Priv."},{"key":"ref_237","doi-asserted-by":"crossref","unstructured":"Liu, Z. (2024, January 29\u201330). A Review of Advancements and Applications of Pre-Trained Language Models in Cybersecurity. Proceedings of the 2024 12th International Symposium on Digital Forensics and Security (ISDFS), San Antonio, TX, USA.","DOI":"10.1109\/ISDFS60797.2024.10527236"},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"969","DOI":"10.32628\/CSEIT25112435","article-title":"The Convergence of IAM and AI: How Large Language Models Are Reshaping Cybersecurity","volume":"11","author":"Banerjee","year":"2025","journal-title":"Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol."},{"key":"ref_239","unstructured":"Islam, M.R. (2024). Generative AI, Cybersecurity, and Ethics, John Wiley & Sons."},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"80218","DOI":"10.1109\/ACCESS.2023.3300381","article-title":"From chatgpt to threatgpt: Impact of generative ai in cybersecurity and privacy","volume":"11","author":"Gupta","year":"2023","journal-title":"IEEE Access"},{"key":"ref_241","doi-asserted-by":"crossref","unstructured":"Szab\u00f3, Z., and Bilicki, V. (2023). A new approach to web application security: Utilizing gpt language models for source code inspection. Future Internet, 15.","DOI":"10.3390\/fi15100326"},{"key":"ref_242","doi-asserted-by":"crossref","unstructured":"Zou, J., Zhang, S., and Qiu, M. (2024, January 16\u201318). Adversarial attacks on large language models. Proceedings of the International Conference on Knowledge Science, Engineering and Management, Birmingham, UK.","DOI":"10.1007\/978-981-97-5501-1_7"},{"key":"ref_243","unstructured":"Chen, F., Wu, T., Nguyen, V., Wang, S., Hu, H., Abuadbba, A., and Rudolph, C. (2024). Adapting to Cyber Threats: A Phishing Evolution Network (PEN) Framework for Phishing Generation and Analyzing Evolution Patterns using Large Language Models. arXiv."},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"42131","DOI":"10.1109\/ACCESS.2024.3375882","article-title":"Devising and detecting phishing emails using large language models","volume":"12","author":"Heiding","year":"2024","journal-title":"IEEE Access"},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"154381","DOI":"10.1109\/ACCESS.2024.3483905","article-title":"ChatPhishDetector: Detecting Phishing Sites Using Large Language Models","volume":"12","author":"Koide","year":"2024","journal-title":"IEEE Access"},{"key":"ref_246","doi-asserted-by":"crossref","unstructured":"Mahendru, S., and Pandit, T. (2024, January 5\u20137). SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection. Proceedings of the 2024 IEEE 7th International Conference on Big Data and Artificial Intelligence (BDAI), Beijing, China.","DOI":"10.1109\/BDAI62182.2024.10692765"},{"key":"ref_247","doi-asserted-by":"crossref","unstructured":"Chataut, R., Gyawali, P.K., and Usman, Y. (2024, January 8\u201310). Can AI Keep You Safe? A Study of Large Language Models for Phishing Detection. Proceedings of the 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC60891.2024.10427626"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/957\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T05:11:36Z","timestamp":1762405896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,4]]},"references-count":247,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["info16110957"],"URL":"https:\/\/doi.org\/10.3390\/info16110957","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,4]]}}}