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A Taxonomy of Intelligent Intrusion Detection Systems For Cybersecurity Using Large Language Models And Agent-Based Artificial Intelligence Cover

A Taxonomy of Intelligent Intrusion Detection Systems For Cybersecurity Using Large Language Models And Agent-Based Artificial Intelligence

Open Access
|Jul 2026

References

  1. Markevych, M., Dawson, M. A review of enhancing intrusion detection systems for cybersecurity using artificial intelligence (AI). In: International Conference Knowledge-Based Organization. Knowledge-Based Organization Conference Proceedings. Romania: Land Forces Academy Publishing House; 2023. pp. 30–37.
  2. Liao, H. J., Lin, C. H. R., Lin, Y. C., Tung, K. Y. Intrusion detection system: A comprehensive review. In: Journal of Network and Computer Applications. Amsterdam: Elsevier; 2013. pp. 16–24.
  3. Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J. Survey of intrusion detection systems: techniques, datasets and challenges. In: Cybersecurity. London: Springer; 2019. pp. 1–22.
  4. Sommer, R., Paxson, V. Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. In: IEEE Symposium on Security and Privacy. California: IEEE; 2010. pp. 305–316.
  5. Kim, A., Park, M., Lee, D. H. AI-IDS: Application of deep learning to real-time web intrusion detection. In: IEEE Access. New York: IEEE; 2020. pp. 70245–70261.
  6. Drewek-Ossowicka, A., Pietrołaj, M., Rumiński, J. A survey of neural networks usage for intrusion detection systems. In: Journal of Ambient Intelligence and Humanized Computing. Berlin: Springer; 2020. pp. 497–514.
  7. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A. A. A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications. USA: IEEE; 2009. pp. 1–6.
  8. Moustafa, N., Slay, J. UNSW-NB15: A comprehensive data set for network intrusion detection systems. In: Military Communications and Information Systems Conference. Australia: IEEE; 2015. pp. 1–6.
  9. Ring, M., Wunderlich, S., Grüdl, D., Landes, D., Hotho, A. A survey of network-based intrusion detection data sets. Computers & Security. Oxford: Elsevier; 2019. pp. 147–167.
  10. Buczak, A. L., Guven, E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials. New York: IEEE; 2016. pp. 1153–1176.
  11. Shone, N., Ngoc, T. N., Phai, V. D., Shi, Q. A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence. New York: IEEE; 2018. pp. 41–50.
  12. Yin, C., Zhu, Y., Fei, J., He, X. A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access. New York: IEEE; 2017. pp. 21954–21961.
  13. Vinayakumar, R., Soman, K. P., Poornachandran, P. Applying deep learning approaches for network traffic prediction. In: International Conference on Advances in Computing, Communications and Informatics. India: IEEE; 2017. pp. 2353–2358.
  14. Ferrag, M. A., Maglaras, L., Moschoyiannis, S., Janicke, H. Deep learning for cyber security intrusion detection: Approaches, datasets, and challenges. Journal of Information Security and Applications. Amsterdam: Elsevier; 2020. pp. 102419.
  15. Goodfellow, I., Shlens, J., Szegedy, C. Explaining and harnessing adversarial examples. In: International Conference on Learning Representations. 2015.
  16. OpenAI. GPT-4 Technical Report. arXiv preprint arXiv: 2303. 08774; 2023.
  17. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y. ReAct: Synergizing reasoning and acting in language models. In: International Conference on Learning Representations (ICLR). 2023.
  18. Xi, Z., Chen, W., Guo, X., et al. The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864; 2023.
  19. Lewis, P., Perez, E., Piktus, A., et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. In: Advances in Neural Information Processing Systems (NeurIPS). 2020.
  20. Gao, L., Ma, X., Lin, J., Callan, J. Precise zero-shot dense retrieval without relevance labels. In: Annual Meeting of the Association for Computational Linguistics (ACL). 2022.
  21. Schick, T., Dwivedi-Yu, J., Dessì, R., et al. Toolformer: Language models can teach themselves to use tools. In: Advances in Neural Information Processing Systems (NeurIPS). 2023.
  22. Amershi, S., Weld, D., Vorvoreanu, M., et al. Guidelines for human-AI interaction. In: Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI). New York: ACM; 2019.
  23. Zziwa, I., Pawar, H., Nartey, C., Dawson, M. GUARD: A Guided AI System for Intrusion Detection and Automated Response in Critical Infrastructure Environments. In: International Conference Knowledge-Based Organization. 2025; 31(1). DOI: 10.2478/kbo-2025-0028.
  24. Quaye, S., Khan, A. H., Tapre, K., Dawson, M. AI-Powered Cybersecurity Models for Training and Testing IoT Devices. Applied Sciences. 2025; 15(24):13073. DOI: 10.3390/app152413073.
DOI: https://doi.org/10.2478/kbo-2026-0076 | Journal eISSN: 2451-3113 (formerly 1843-6722) | Journal ISSN: 1843-6722
Language: English
Page range: 1 - 11
Published on: Jul 5, 2026
Published by: Nicolae Balcescu Land Forces Academy
In partnership with: Paradigm Publishing Services
Publication frequency: 3 issues per year

© 2026 Samson Quaye, Maurice Dawson, Enkel Hoxha, published by Nicolae Balcescu Land Forces Academy
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.