Network packet

Engineering

Transport engineering

Support vector machine

Network traffic control

Traffic classification

Real-time computing

Encryption

Quality of service

Data mining

Computer science

Deep packet inspection

Computer network

Traffic generation model

Naive Bayes classifier

Artificial intelligence

Floating car data

Traffic congestion

Machine learning

Computer Networks

The growing demand for high-speed transmission rates in recent years attracted research in new mechanisms for network traffic characterization and classification. Their inadequate treatment degrades the performance of important operational schemes, such as Network Survivability, Traffic Engineering, Quality of Service (QoS), and Dynamic Access Control, among others. The most common methods for traffic classification are Deep Packet Inspection (DPI) and port based classification. However, those methods are becoming obsolete, as increasingly more traffic is being encrypted and applications are using dynamic ports or ports originally assigned to other popular applications. This paper presents a classification module for video streaming traffic, based on machine learning, as a solution for network schemes that require adequate real-time traffic treatment. The module adopts a new approach for the relaxation of the hypothesis of independence between the attributes of the Naive Bayes algorithm. The results show that the proposed module is a promising alternative to be applied in real-time scenarios.

An innovative approach for real-time network traffic classification