TCP Traffic Classification Using Markov Models

This paper presents a novel traffic classification approach which classifies TCP connections with help of observable Markov models. As traffic properties, payload length, direction, and position of the first packets of a TCP connection are considered. We evaluate the accuracy of the classification approach with help of packet traces captured in a real network, achieving higher accuracies than the cluster-based classification approach of Bernaille [1]. As another advantage, the complexity of the proposed Markov classifier is low for both training and classification. Furthermore, the classification approach provides a certain level of robustness against changed usage of applications.

• Type: Journal
• Year: 2010
• Where: TMA
• Authors: Gerhard Münz, Hui Dai, Lothar Braun, Georg Carle
• Keywords: Classification Approach, Management, Tcp Connection, TMA 2010, Traffic Classification Approach