Class Imbalance in Network Traffic Classification: An Adaptive Weight Ensemble-of-Ensemble Learning Method
[EN]Network Traffic Classification (NTC) serves as a crucial element in network management, and the rapid progress in machine learning has inspired the utilization of learning methods to discern network traffic. The inherent characteristics of network traffics result in uneven class distributions wh...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/166192 |
| Acceso en línea: | http://hdl.handle.net/10366/166192 |
| Access Level: | acceso abierto |
| Palabra clave: | Telecommunication traffic Ensemble learning Robustness Adaptation models Accuracy Classification algorithms Training Streams Costs Boosting 1203.04 Inteligencia Artificial |
| Sumario: | [EN]Network Traffic Classification (NTC) serves as a crucial element in network management, and the rapid progress in machine learning has inspired the utilization of learning methods to discern network traffic. The inherent characteristics of network traffics result in uneven class distributions when datasets are shaped, creating a phenomenon known as class imbalance. This phenomenon has garnered growing attention across various research fields. Despite encountering performance setbacks attributed to class imbalance, this challenge remains inadequately examined in the realm of network traffic classification. This paper introduces the Adaptive Weight Ensemble-of-Ensemble Learning (AWEE) method as an innovative solution to this challenge. The AWEE integrates multiple ensemble layers with a dynamic weight adjustment mechanism, showcasing the collaborative intelligence of diverse modeling strategies. Using a sliding window-based validation approach, the model enhances adaptability and robustness to address concept drift in dynamic data streams. Experimental studies on benchmark datasets demonstrate the superior performance of AWEE (achieving an outstanding accuracy rate of over 98%), highlighting its effectiveness in handling class imbalance challenges across diverse network traffic scenarios. AWEE, outperforms competitive methods, including algorithmic-level, cost-sensitive, and data-level techniques, showcasing its robustness and superior performance in addressing class imbalance challenges across a wide range of network traffic scenarios. |
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