University Network Traffic Patterns Prediction Using LSTM and RBM

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@article{1706912,
      author = {OMONIYI Victoria Ibiyemi, AKINTOKUN Oluyomi Kolawole},
      title = {University Network Traffic Patterns Prediction Using LSTM and RBM},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {7},
      pages = {254-262},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706912.pdf},
      abstract = {Accurate prediction of university network traffic is essential for efficient resource management, resource optimization, security enhancement, and optimal user experience. Traditional statistical methods often struggle with network traffic data's complex, nonlinear, and time-varying nature. These challenges have been successfully addressed through recent advancements in deep learning, particularly the development and application of Long Short-Term Memory (LSTM) networks. This paper introduces a novel approach to network traffic prediction by integrating Long Short-Term Memory (LSTM) networks and Restricted Boltzmann Machines (RBM). LSTM is a specific architecture within the family of recurrent neural networks, and it is adapted to predict network traffic Patterns in dynamic university environments. Comprehensive experiments are carried out utilizing real-world network traffic data collected from university environments. The findings reveal that the proposed LSTM-based model performs robustly across all major metrics, achieving low values for Test Loss, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), along with a high R? score, signifying outstanding accuracy and generalization capabilities. LSTM proves to be highly capable of handling time-series data or sequence-based tasks. },
      keywords = {Network-Traffic, Prediction, Long Short-Term Memory, Restricted Boltzmann Machines},
      month = {January},
  }