LAUSR

Traffic classification is considered an important research area due to the increasing demand in network users. It not only effectively improve the network service identifications and security issues of the traffic network, but also provide robust accuracy and efficiency in different Internet application behaviors and patterns. Several traffic classification techniques have been proposed and applied successfully in recent years. However, the existing literature lack of comprehensive survey which could provide an overview and analysis towards the recent developments in network traffic classification. To this end, this survey presents a comprehensive investigation on traffic classification techniques by carefully reviewing existing methods from a new perspective. We comprehensively discuss the procedures and datasets for traffic classification. Additionally, traffic criteria are proposed, which could be beneficial to assess the effectiveness of the developed classification algorithm. Then, the traffic classification techniques are discussed in detail. Then, we thoroughly discussed the machine learning (ML) methods for traffic classification. For researcher’s convenience, we present the traffic obfuscation techniques, which could be helpful for designing a better classifier. Finally, key findings and open research challenges for network traffic classification are identified along with recommendations for future research directions. In sum, this survey fills the gap of existing surveys and summarizes the latest research developments in traffic classification.