MACHINE LEARNING ALGORITHMS IN NETWORK TRAFFIC CLASSIFICATION - A SURVEY

Abstract

Effective NTC plays a vital role in enhancing bandwidth efficiency, ensuring network security, and maintaining Quality of Service (QoS) in todays communication systems. Conventional methods like port-based as well as payload-based classification are no more reliable because of the rise of encoded traffic and dynamic applications. As an alternative, Machine Learning (ML) approaches can automatically identify patterns in the statistical characteristics of network flows, offering more flexibility and accuracy. This paper presents a survey of Machine Learning that was used in NTC. The main focus on latest advances such as Protocol Detection, Traffic Behavior Analysis and Application Identification.Resent Trends, Evaluation Metrices, Benchmark Datasets and Machine Learning Algorithms are the core things that was discussed in this review. Along with Class Imbalance, Concept drift and encrypted traffic, these kinds of problem were also discussed in it. Beside these things, to understand the ML algorithms and make baseline benchmark as KDD dataset to represent comparative analysis.