Internet traffic classification has been the subject of intensive study since the birth of the Internet itself. Indeed, the evolution of approaches for traffic classification can be associated with the evolution of the Internet itself and with the adoption of new services and the emergence of novel applications and communication paradigms. Throughout the years many approaches have been proposed for addressing technical issues imposed by such novel services. Deep-Packet Inspection (DPI) has been a very important research topic within the traffic classification field and its concept consists of the analysis of the contents of the captured packets in order to accurately and timely discriminate the traffic generated by different Internet protocols. DPI was devised as a means to address several issues associated with port-based and statistical-based classification approaches in order to achieve an accurate and timely traffic classification. Many research works proposed different DPI schemes while many open-source modules have also become available for deployment. Surveys become then valuable tools for performing an overall analysis, study and comparison between the several proposed methods. In this paper we present a survey in which a complete and thorough analysis of the most important open-source DPI modules is performed. Such analysis comprises an evaluation of the classification accuracy, through a common set of traffic traces with ground truth, and of the computational requirements. In this manner, this survey presents a technical assessment of DPI modules and the analysis of the obtained evaluation results enable the proposal of general guidelines for the design and implementation of more adequate DPI modules.

A Survey of Payload-Based Traffic Classification Approaches

Machine Learning (ML) for classifying IP traffic has relied on the analysis of statistics of full flows or their first few packets only. However, automated QoS management for interactive traffic flows requires quick and timely classification well before the flows finish. Also, interactive flows are often long-lived and should be continuously monitored during their lifetime. We propose to achieve this by using statistics derived from sub-flows--a small number of most recent packets taken at any point in a flow's lifetime. Then, the ML classifier must be trained on a set of sub-flows, and we investigate different sub-flow selection strategies. We also propose to augment training datasets so that classification accuracy is maintained even when a classifier mixes up client-to-server and server-to-client directions for applications exhibiting asymmetric traffic characteristics. We demonstrate the effectiveness of our approach with the Naive Bayes and C4.5 Decision Tree ML algorithms, for the identification of first-person-shooter online game and VoIP traffic. Our results show that we can classify both applications with up to 99% Precision and 95% Recall within less than 1 s. Stable results are achieved regardless of where within a flow the classifier captures the packets and the traffic direction.

Timely and continuous machine-learning-based classification for interactive IP traffic

In this paper we describe the construction and performance of classifiers able to identify Variable Rate VoIP traffic flows rapidly, reliably and independently of the application version that generated it. We show that features calculated on short sequences of packets extracted from the flow (sub-flows) are sufficient to identify VoIP flows with Recall of 99% and Precision of 90%. The features we used are based on mean packet length, autocorrelation and the ratio of data transmitted in either direction of a bi-directional flow. Even though the codecs we use to generate VoIP traffic are quite different, we show that by using selected features that capture the nature of variable bit rate voice traffic, a classifier trained on traffic generated by one version of VoIP can reliably recognize traffic generated by another version.

Rapid and generalized identification of packetized voice traffic flows

Identification of Skype traffic using machine learning is an area of current research interest. Previous Skype classifiers have usually been reliant on version specific features. Consequently, a classifier that works for a specific version of Skype is unlikely to work for successive versions. Classification of Skype has been successful with previous research showing 98% precision for Skype version 3. But classification using Skype version 3 attributes were not successful in identifying Skype version 4. In our experiments, we use machine learning to classify Skype version 3 and 4 for a subflow size of 100 with characteristics common to both versions. We discuss attributes that are generic to Skype and show that Skype can be identified with 97% precision and 86% recall.

/pdf/generic-attributes-for-skype-identification-using-machine-r9604n8cqm.pdf

Generic Attributes for Skype Identification Using Machine Learning

Scalable classification of QoS for real-time interactive applications from IP traffic measurements

BitTorrent is one of the dominant traffic generating applications in the Internet today The ability to identify BitTor-rent traffic in real-time could allow network operators to better manage network traffic and provide a better service to their customers In this paper we analyse the statistical properties of BitTorrent traffic and select four features that can be used for real-time classification using Machine Learning techniques We then train and test a classifier using the C45 algorithm Our results show that based on statistics calculated on 150-packet sub-flows, we can classify BitTorrent traffic with Recall of 982% and Precision of 965% We then show that 981% of sub-flows from other client-server bulk transfer applications are correctly classified as non-BitTorrent

https://researchbank.swinburne.edu.au/file/b53eda08-3685-43cc-8189-ca1348293549/1/PDF (Published version).pdf

Rapid Identification of BitTorrent traffic

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Rapid Identification of BitTorrent traffic