A Comprehensive Investigation Into the Implementation of Machine Learning Solutions for Network Traffic Classification

Machine learning-based network traffic categorization has become a crucial field of study because of the rising intricacy of Internet services and the expanding usage of en-cryption. A thorough examination of the application of machine learning techniques for automated network traffic classification and analysis is given by this systematic study. A comprehensive review of the literature was done using many large scientific databases in order to find pertinent research that used machine learning methods for traffic categorization. 52 papers were chosen for final assessment out of over 700 initial results, based on relevance, rigour, and creativity. Important models, characteristics, datasets, algorithms, and limits related to traffic categorization are summarised, along with significant trends. Analysis reveals that larger traffic datasets, more feature sets, and a greater use of deep learning structures have all contributed to enhanced model resilience and accuracy. Nevertheless, problems still exist with real-time deployment, encrypted traffic analysis, and model generalisation across networks. Intelligent traffic categorization systems have the potential to improve network security, provide better quality of service, and provide insights into usage patterns, but additional advancements are necessary for real-world applicability.