A multilevel taxonomy and requirements for an optimal traffic‐classification model

Abstract

Identifying Internet traffic applications is essential for network security and management. The steady emergence of new Internet applications, together with the use of encryption and obfuscation techniques, ensures that traffic classification remains a hot research topic. Much research has been devoted to this topic by the research community in the last decade. However, an optimal traffic classification model has yet to be defined. Many techniques and formats have been described, with the current literature therefore lacking appropriate benchmarks expressed in a consistent terminology. Moreover, existing surveys are outdated and do not include many recent advances in the field. In this article, we present a systematic multilevel taxonomy that covers a broad range of existing and recently proposed methods, together with examples of vendor classification techniques. Our taxonomy assists in defining a consistent terminology. It could be useful in future benchmarking contexts by characterizing and comparing methods at three different levels. From this perspective, we describe key features and provide design hints for future classification models, while emphasizing the main requirements for promoting future research efforts. To motivate researchers and other interested parties, we collect and share data captured from real traffic, using two models to protect data privacy.

A class-oriented feature selection approach for multi-class imbalanced network traffic datasets based on local and global metrics fusion

“…A lot of network applications run on the Internet and generate a large amount of traffic every day [1]. It is a daunting task to manage the huge and mixture traffic.…”

A survey of methods for encrypted traffic classification and analysis

“…We choose the multilevel taxonomy by Khalife et al . , which provides a detailed categorization of traffic classification methods. This taxonomy is uniquely descriptive and allows us to efficiently categorize all our surveyed classification methods.…”

Traffic classification for managing Applications’ networking profiles

“…Traffic classification has been the topic of a number of reviewing and surveying papers, as well as papers describing issues and providing comparisons among different techniques .…”

“…The obtained accuracy results were compared in order to analyze the advantages and limitations of each approach aiming to provide recommendations for mitigating the limitations. In the survey presented in , the authors proposed a multilevel taxonomy describing traffic classifiers at three different levels (classifications’ inputs, outputs and techniques) in order to compare recent achievements and future traffic classifiers. They also provided design hints for optimal models aiming at future research efforts.…”