Markevych, M., Dawson, M. “A Review of Enhancing Intrusion Detection Systems for Cybersecurity Using Artificial Intelligence (AI)”. International Conference KNOWLEDGE-BASED ORGANIZATION. Sibiu: Vol. XXIX, No. 2023. DOI: 10.2478/kbo-2023-0000.
Dawson, M., Bacius, R., Gouveia, L. B., & Vassilakos, A. “Understanding the challenge of cybersecurity in critical infrastructure sectors”. Land Forces Academy Review. March, 2021; 26(1): 69–75. https://sciendo.com/article/10.2478/raft-2021-0011.
Kumar, G., Kumar, K., & Sachdeva, M. (2010). “The use of artificial intelligence based techniques for intrusion detection: a review”. Artificial Intelligence Review. December, 2010; 34(4), 369–387. https://link.springer.com/article/10.1007/s10462-010-9179-5.
Sommer, R., & Paxson, V. “Outside the Closed World: On Using Machine Learning for Network Intrusion Detection”. In: IEEE Symposium on Security and Privacy. 2010. 305–316.
Kim, A., Park, M., & Lee, D. H. “AI-IDS: Application of Deep Learning to Real-Time Web Intrusion Detection”. IEEE Access. 2020; 8, 70245–70261. https://ieeexplore.ieee.org/document/9063416.
Ferrag, M. A., Ndhlovu, M., Tihanyi, N., Cordeiro, L. C., Debbah, M., Lestable, T., & Thandi, N. S. “Revolutionizing cyber threat detection with large language models: A privacy-preserving BERT-based lightweight model for IoT/IIoT devices” [Preprint]. 2024. arXiv. https://arxiv.org/abs/2306.14263.
Seliya, N., Abdollah Zadeh, A., & Khoshgoftaar, T. M. “A literature review on one-class classification and its potential applications in big data”. Journal of Big Data. 2021; 8, Article 122. https://doi.org/10.1186/s40537-021-00514-x.
Sarhan, M., Kulatilleke, G., Lo, W. W., Layeghy, S., & Portmann, M. “DOC-NAD: A hybrid deep one-class classifier for network anomaly detection” [Preprint]. 2022. arXiv. https://arxiv.org/abs/2212.07558.
Seliya, N., Abdollah Zadeh, A., & Khoshgoftaar, T. M. “A literature review on one-class classification and its potential applications in big data”. Journal of Big Data. 2021; 8, Article 122. https://doi.org/10.1186/s40537-021-00514-x.
Daoud, L., & Huen, H. “Performance study of software-based encrypting data at rest”. In: Proceedings of the 37th International Conference on Computers and Their Applications (EPiC Series in Computing, Vol. 82: 122–130). 2022. EasyChair. Retrieved from https://easychair.org/publications/paper/GHB6/open?utm_source=chatg.
Singh, J., Rani, S., & Kumar, V. “Role-based access control (RBAC) enabled secure and efficient data processing framework for IoT networks”. International Journal of Communication Networks and Information Security. 2024. 16(2): 19-32. https://ijcnis.org/.
Manujosephv. (n.d.). “GitHub - manujosephv/pytorch_tabular: A standard framework for modelling Deep Learning Models for tabular data.” GitHub. https://github.com/manujosephv/pytorch_tabular