Designing an Intrusion Detection Framework to Protect Academic and Administrative Platforms at Copperstone University | IJCT Volume 12 – Issue 5 | IJCT-V12I5P73

The growing frequency of cyberattacks in higher education highlights the urgent need for intelligent and adaptive defense systems. This research presents the design, implementation, and evaluation of an artificial intelligence–driven intrusion detection framework developed for Copperstone University’s academic and administrative platforms. Using a mixed-methods approach, the system combined benchmark intrusion datasets with live network traffic to train deep learning models such as Convolutional Neural Networks, Recurrent Neural Networks, and hybrid ensembles for real-time anomaly detection. The findings showed that the framework effectively identified malicious activities with high accuracy and reduced false positives, offering improved visibility and a faster response to threats. Ethical considerations, including data anonymization, institutional approval, and secure model deployment, guided the development of the system. The study concludes that deep learning–based intrusion detection can significantly enhance cybersecurity resilience in universities. Future recommendations include integrating federated learning, refining model explainability, and extending the framework to other resource-limited academic environments.

In conclusion, the evaluation confirms that the Copperstone cyber Shield IDS architecture reliably realizes its core objectives: rapid initialization and consolidation of baseline rules, robust anomaly and deep learning detection, real-time logging of threat events, and structured alert management with analyst assignment and filtering. The observed dashboards validate that the system presents both high-level network posture metrics and drill-down capabilities for incident investigation, while AI modules operate in concert to escalate high-risk alerts with confidence scoring. The system’s performance across various test cases demonstrates that the integration of signature, anomaly, and deep learning modules functions coherently and resiliently under typical load. Given these positive findings, the system is well positioned for further extension, deployment, and institutional adoption as a dependable, adaptive intrusion detection solution.

1.T. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,” IEEE Trans. Emerg. Topics Comput. Intell., vol. 2, no. 1, pp. 41–50, Feb. 2018. 2.S. Vinayakumar, I. S. Rawat, M. Alazab, and K. P. Soman, “Deep learning approach for intelligent intrusion detection system,” IEEE Access, vol. 7, pp. 41525–41550, 2019. 3.X. Gao, C. Shan, C. Hu, Z. Niu, and Z. Liu, “An adaptive ensemble machine learning model for intrusion detection,” IEEE Access, vol. 7, pp. 82512–82521, 2019. 4.B. A. Tama, M. Comuzzi, and K.-H. Rhee, “TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system,” IEEE Access, vol. 7, pp. 94497–94507, 2019. 5.G. Andresini, A. Appice, N. Di Mauro, C. Loglisci, and D. Malerba, “Multi-channel deep feature learning for intrusion detection,” IEEE Access, vol. 8, pp. 53346–53359, 2020. 6.M. A. Ferrag, L. Maglaras, S. Moschoyiannis, and H. Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study,” J. Inf. Secur. Appl., vol. 50, 2020, Art. no. 102419. 7.H. Liu, B. Lang, M. Liu, and H. Yan, “CNN and RNN based deep learning methods for network intrusion detection,” Computers & Security, vol. 102, 2021, Art. no. 102289. 8.M. A. Khan, “HCRNNIDS: Hybrid convolutional recurrent neural network-based network intrusion detection system,” Processes, vol. 9, no. 5, p. 834, 2021. 9.S. M. Kasongo and Y. Sun, “A deep learning method with filter/wrapper-based feature engineering for wireless intrusion detection system,” IEEE Access, vol. 7, pp. 38597–38607, 2019. 10.S. M. Kasongo, “A deep learning technique for intrusion detection system using a recurrent neural networks–based framework,” Computer Communications, vol. 208, pp. 113–125, 2023. 11.X. Deng, J. Zhu, X. Pei, L. Zhang, Z. Ling, and K. Xue, “Flow topology-based graph convolutional network for intrusion detection in label-limited IoT networks,” IEEE Trans. Netw. Serv. Manage., vol. 20, no. 1, pp. 684–696, Mar. 2023. 12.J. Long, W. Liang, K.-C. Li, Y. Wei, and M. D. Marino, “A regularized cross-layer ladder network for intrusion detection in industrial Internet of Things,” IEEE Trans. Ind. Informatics, vol. 19, no. 2, pp. 1747–1755, Feb. 2023. 13.A. Oseni et al., “An explainable deep learning framework for resilient intrusion detection in IoT-enabled transportation networks,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 1, pp. 1000–1014, Jan. 2023. 14.J. A. de Oliveira, R. R. Goldschmidt, A. C. Drummond, and E. C. Gurjão, “F-NIDS—A network intrusion detection system based on federated learning,” Computer Networks, vol. 236, 2023, Art. no. 110010. 15.S. Hajj, J. Azar, J. Bou Abdo, J. Demerjian, C. Guyeux, A. Makhoul, and D. Ginhac, “Cross-layer federated learning for lightweight IoT intrusion detection systems,” Sensors, vol. 23, no. 16, p. 7038, 2023. 16.X. Luo, Y. Li, and G. Li, “E-GraphSAGE: A federated edge learning framework for network intrusion detection,” in Proc. IEEE/IFIP NOMS, 2022, pp. 1–9. 17.S. Li, H. Liu, Y. Wang, and T. Li, “GNN-IDS: Graph neural network-based intrusion detection system,” in Proc. ACM Int. Conf. Netw. Syst. Secur., 2024, pp. 1–11. 18.S. I. Popoola et al., “Federated deep learning for intrusion detection in consumer IoT networks,” IEEE Trans. Consum. Electron., vol. 70, no. 1, pp. 1–10, 2024. 19.M. J. Idrissi, H. Alami, A. El Mahdaouy, and I. Berrada, “Fed-ANIDS: Federated learning for anomaly-based network intrusion detection systems,” Expert Syst. Appl., vol. 234, 2023, Art. no. 121000. 20.R. Lazzarini, M. Vecchio, and F. Antonelli, “Federated learning for IoT intrusion detection,” AI, vol. 4, no. 3, pp. 675–694, 2023.

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