Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models
Accurate prediction of network traffic patterns is essential for optimizing network resource allocation, managing congestion, and strengthening cybersecurity. This study examines the effectiveness of four machine learning models—Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Gated R...
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| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03021.pdf |
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| author | Wang Yuxin |
| author_facet | Wang Yuxin |
| author_sort | Wang Yuxin |
| collection | DOAJ |
| description | Accurate prediction of network traffic patterns is essential for optimizing network resource allocation, managing congestion, and strengthening cybersecurity. This study examines the effectiveness of four machine learning models—Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM)—in forecasting traffic patterns using both web-based and real-world datasets. The models are evaluated based on their generalization accuracy, as measured by Mean Absolute Percentage Error (MAPE), computational efficiency, and their ability to capture underlying traffic dynamics. Results indicate that GRU surpasses SVR and LSTM in terms of prediction accuracy and computational speed, while Bidirectional LSTM demonstrates superiority in capturing long-term dependencies across extended periods. These findings underscore the significant potential of deep learning models, particularly GRU and Bidirectional LSTM, in improving the precision and reliability of network traffic predictions. The study offers insights into the strengths and limitations of each model, contributing to the ongoing development of more robust and efficient network traffic forecasting methods. |
| format | Article |
| id | doaj-art-5c3782854f0643fab578d89db6d64fb3 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-5c3782854f0643fab578d89db6d64fb32025-02-07T08:21:12ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700302110.1051/itmconf/20257003021itmconf_dai2024_03021Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM ModelsWang Yuxin0College of Arts and Sciences, New York UniversityAccurate prediction of network traffic patterns is essential for optimizing network resource allocation, managing congestion, and strengthening cybersecurity. This study examines the effectiveness of four machine learning models—Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM)—in forecasting traffic patterns using both web-based and real-world datasets. The models are evaluated based on their generalization accuracy, as measured by Mean Absolute Percentage Error (MAPE), computational efficiency, and their ability to capture underlying traffic dynamics. Results indicate that GRU surpasses SVR and LSTM in terms of prediction accuracy and computational speed, while Bidirectional LSTM demonstrates superiority in capturing long-term dependencies across extended periods. These findings underscore the significant potential of deep learning models, particularly GRU and Bidirectional LSTM, in improving the precision and reliability of network traffic predictions. The study offers insights into the strengths and limitations of each model, contributing to the ongoing development of more robust and efficient network traffic forecasting methods.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03021.pdf |
| spellingShingle | Wang Yuxin Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models ITM Web of Conferences |
| title | Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models |
| title_full | Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models |
| title_fullStr | Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models |
| title_full_unstemmed | Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models |
| title_short | Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models |
| title_sort | advanced network traffic prediction using deep learning techniques a comparative study of svr lstm gru and bidirectional lstm models |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03021.pdf |
| work_keys_str_mv | AT wangyuxin advancednetworktrafficpredictionusingdeeplearningtechniquesacomparativestudyofsvrlstmgruandbidirectionallstmmodels |