Bace, R. G., & Mell, P. (2001). NIST special publication on intrusion detection systems (SP 800-31). National Institute of Standards and Technology. Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. Davies, I. N., Cookey, I. B., & Godspower, O. (2025). Biometric signal processing for security and privacy: Trends, challenges, and future directions. International Journal of Computer Science and Mathematical Theory, 11(11), 76-90. Davies, I. N., Ene, D., Cookey, I. B., Godspower, O., & Deedam, F. B. (2026). An intelligent machine learning framework for password strength and security system. International Journal of Mathematics and Computer Research, 14(4), 6331-6338. Dietterich, T. G. (2000). Ensemble methods in machine learning. In Proceedings of the International Workshop on Multiple Classifier Systems (pp. 1-15). Springer. Dongmei, Z., Yaxing, W., & Hongbin, Z. (2022). A situation awareness approach for network security using the fusion model. Mobile Information Systems, 2022, Article 6214738. Duan, X., Ziming, T., & Meng, W. (2023). Situation awareness in intelligent sensing and industrial automation. In Proceedings of the 2023 International Conference on Intelligent Sensing and Industrial Automation (Article No. 24, pp. 1-5). Ganaie, M. A., Hu, M., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151. Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural Computation, 12(10), 2451-2471. Godspower, O., & Anireh, V. I. E. (2022). Evolution of supercomputer architecture: A survey. International Journal of Computer Science and Mobile Applications, 10(7), 16-26. Godspower, O., Nwiabu, N., & Anireh, V. I. E. (2020). Frequent itemset mining using two dimensional transaction reduction. Journal of Scientific and Engineering Research, 7(7), 7-14. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157-1182. Hassan, M. M., Gumaei, A., Al-Rakhami, M., & Alghamdi, A. (2018). A deep learning approach for network intrusion detection using recurrent neural networks. IEEE Access, 6, 21954-21961. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. Kim, G., Lee, S., & Kim, S. (2016). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications, 41(4), 1690-1700. Lakhina, A., Crovella, M., & Diot, C. (2004). Diagnosing network-wide traffic anomalies. ACM SIGCOMM Computer Communication Review, 34(4), 219-230. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. Moustafa, N., & Slay, J. (2015). The UNSW-NB15 dataset: A comprehensive data repository for network intrusion detection systems. In 2015 Military Communications and Information Systems Conference (pp. 1-6). IEEE. Moustafa, N., & Slay, J. (2016). UNSW-NB15: A comprehensive data set for network intrusion detection systems. In 2016 IEEE 11th International Conference on Availability, Reliability and Security (pp. 53-58). IEEE. IJCSMT Roesch, M. (1999). Snort: Lightweight intrusion detection for networks. In Proceedings of the 13th USENIX Conference on System Administration (LISA '99) (pp. 229-238). USENIX Association. Scarfone, K., & Mell, P. (2007). Guide to intrusion detection and prevention systems . NIST Special Publication 800-94. National Institute of Standards and Technology. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE Symposium on Security and Privacy (pp. 305-316). IEEE. Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2017). Applying convolutional neural network for network intrusion detection. In Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (pp. 1222-1228). IEEE. Wang, W. (2024). Cognitive differences in network security based on mathematical modeling based on ensemble learning algorithm. Journal of Cybersecurity and Cognitive Studies, 9(2), 115-132. Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access, 5, 21954-21961. Zhang, R. C., Zhang, Y. C., Liu, J., & Fan, Y. D. (2019). Network security situation prediction method using improved convolution neural network. Computer Engineering and Applications, 55(6), 86-93. IJCSMT Authors Oraye Godspower Oraye Godspower received his Bachelor of Science (B.Sc.) degree in Mathematics and Computer Science from Rivers State University of Science and Technology , Port Harcourt, Nigeria, in 2013, and his Master of Science (M.Sc.) degree in Computer Science from Rivers State University , Port Harcourt, Nigeria, in 2021. He completed his Doctor of Philosophy (Ph.D.) in Computer Science at Rivers State University in 2024. He began his academic career as a Graduate Assistant in the Department of Computer Science, Rivers State University, in 2019 and currently serves as a Lecturer II. He is a member of the Computer Professionals of Nigeria . He has taught numerous courses across the computer science curriculum. His research interests include network security, intrusion detection systems, deep learning, ensemble methods, and frequent itemset mining. He has published in international journals on topics including supercomputer architecture, biometric signal processing, and frequent itemset mining. His Google Scholar and ResearchGate profiles provide further details of his publications and research activities. He is the corresponding author and can be contacted at
[email protected]. Professor N. D. Nwiabu Professor N. D. Nwiabu is a Professor of Software Engineering in the Department of Computer Science, Rivers State University, Port Harcourt, Nigeria. He holds advanced degrees in Computer Science with specialization in artificial intelligence and cognitive engineering. His research focuses on artificial intelligence, cognitive engineering, machine learning, and their applications to network security and decision support systems. He has supervised numerous postgraduate theses and published extensively in peer-reviewed journals and conference proceedings. His scholarly work contributes to advancing computational intelligence and cognitive approaches for solving real-world security and software engineering challenges. Professor D. Matthias Professor D. Matthias pursued his Bachelor of Science (B.Sc.) degree in Mathematics and Computer Science at the University of Port Harcourt, Rivers State, Nigeria. He obtained his Master of Technology (M.Tech.) degree from the Federal University of Technology, Owerri, Nigeria, and his Doctor of Philosophy (Ph.D.) degree from Rivers State University of Science and Technology, Port Harcourt, Nigeria. He is currently a Professor of Computer Science in the Department of Computer Science, Rivers State University (formerly Rivers State University of Science and Technology). He has been a member of the Computer Professionals of Nigeria since 2010 and a life member of the Nigeria Computer Society since 2012. He has numerous scholarly publications in reputed local and international journals. His main research focuses on scientific computing, database systems, and software development. Associate Professor E. O. Bennett Associate Professor E. O. Bennett graduated with a Bachelor of Science (B.Sc.) degree in Computer Science from Rivers State University, Port Harcourt, Nigeria, in 1998. He obtained his Master of Science (M.Sc.) and Doctor of Philosophy (Ph.D.) degrees from the University of Port Harcourt in 2008 and 2014, respectively. Currently, he is an Associate Professor and a Lecturer in the Department of Computer Science, Rivers State University, Port Harcourt, Nigeria. He is a member of the Computer Professionals of Nigeria . He has published over 50 research papers in reputed international journals. His research focuses on algorithms, parallel computing, distributed computing, and intelligent computing.