Hybrid Data Fusion and Deep Learning for Dynamic Risk Index Modeling in Secure Learning Management Systems

The explosive growth of online education platforms has led to increased exposure to cybersecurity threats, which makes secure Learning Management Systems (LMS) a critical requirement. However, the current methods often can't capture user behavior risk and network-level attack patterns at the same time, which causes the threat to be incomplete. This study presents a dynamic cyber risk prediction model by fusing log information of LMS behavior with network intrusion information in the CICIDS2017 dataset. The goal is to create an AI-based model that is able to perform real-time risk assessment using a Dynamic Risk Index (DRI). The methodology includes the combination of feature engineering, hybrid data fusion, machine learning, deep learning (LSTM, DNN), and anomaly detection methods. Experimental results demonstrate that the proposed model achieves an accuracy of 97.6%, an F1-score of 96.9%, and an AUC of 98.5%, outperforming state-of-the-art methods. The robustness and significance of the framework are confirmed by ablation and statistical analyses. The overall study concludes that combining behavioral and network intelligence with dynamic risk scoring improves cyber threat detection and proactive security management in e-learning environments.

@article{T2026,
  title     = {Hybrid Data Fusion and Deep Learning for Dynamic Risk Index Modeling in Secure Learning Management Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Vani T and S. Sathya},
  doi       = {10.14569/IJACSA.2026.0170672},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170672}
}