Enhanced Network Traffic Classification with Machine Learning Algorithms
Ihab Ahmed Najm, Ahmed Hikmat Saeed, Bilal Ahmad, Saadaldeen Rashid Ahmed, Ravi Sekhar, Pritesh Shah, B. S. Veena
Abstract
Network traffic classification plays a critical role in maintaining the security and efficiency of modern computer networks. Existing techniques often have problems effectively identifying and establishing network traffic patterns. This research seeks to fill this gap by offering an updated approach to network traffic classification using machine learning algorithms. Our research extends upon earlier studies by focusing on robust feature engineering techniques and applying a broad assortment of machine learning algorithms, including decision trees, random forests, support vector machines, and recurrent neural networks. We employ a comprehensive dataset containing diverse network traffic situations to train. We demonstrate the efficacy of our approach by achieving promising accuracy rates: 93% for decision trees, 97.89% for random forests, 91% for support vector machines, and 89.49% for recurrent neural networks. Our findings emphasize the promise of machine learning for addressing real-world network traffic classification challenges.