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Prediction of University Network Traffic Behaviour using LSTM and OGRU

Domain:

digital infrastructure

Record type:

paper
Creator:
OmoOye
Publisher:
Zenodo
Host:avatar

Recently, the surge in Internet usage across academic institutions has created a pressing need for effective and intelligent methods of predicting network traffic. These mechanisms are vital for efficient resource management, early detection of irregularities, strengthening cybersecurity, maintaining uninterrupted connectivity for both academic and administrative activities, and improving service quality. This research explores the application of Long Short-Term Memory (LSTM) and Optimized Gated Recurrent Unit (OGRU) models to study and predict network traffic trends within university settings. Both LSTM and OGRU are advanced deep learning frameworks designed to recognize temporal patterns in sequential data, making them well-suited for modelling intricate and dynamic network behaviours. The system undergoes some stages, such as Data collection, Data Pre-processing, Training stage, Testing Stage, Prediction stage, and Evaluation.  The data were splitted by assigning 70% to training, 10% validation, and 20% to testing.  The findings reveal that the proposed LSTM-based model performs robustly across all major metrics, achieving low values for (Test Loss: 0.000595), Mean Absolute Error (MAE: 0.009475), Mean Squared Error (MSE: 0.000584), and Root Mean Squared Error (RMSE: 0.024170), along with a high R² score, signifying outstanding accuracy and generalization capabilities. This indicates that LSTM has stronger predictive power better than the other model used.

Visit

doi.org

Tags

Network TrafficPredictionLong Short-Term MemoryOptimized Gated Recurrent Unit

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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