Recognition of traffic generated by WebRTC communication

Sabina Baraković,Jasmina Baraković Husić,Nadina Ajdinović,Semina Nurkić

doi:10.54327/set2021/v1.i1.8

Sabina Baraković, Jasmina Baraković Husić + Show 2 more

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https://doi.org/10.54327/set2021/v1.i1.8

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Abstract

Network traffic recognition serves as a basic condition for network operators to differentiate and prioritize traffic for a number of purposes, from guaranteeing the Quality of Service (QoS), to monitoring safety, as well as monitoring and detecting anomalies. Web Real-Time Communication (WebRTC) is an open-source project that enables real-time audio, video, and text communication among browsers. Since WebRTC does not include any characteristic pattern for semantically based traffic recognition, this paper proposes models for recognizing traffic generated during WebRTC audio and video communication based on statistical characteristics and usage of machine learning in Weka tool. Five classification algorithms have been used for model development, such as Naive Bayes, J48, Random Forest, REP tree, and Bayes Net. The results show that J48 and BayesNet have the best performances in this experimental case of WebRTC traffic recognition. Future work will be focused on comparison of a wide range of machine learning algorithms using a large enough dataset to improve the significance of the results.

Highlights

Web Real-Time Communication (WebRTC) is an open-source project that enables direct real-time communication in web browsers and mobile applications through a simple Application Programming Interface (API) [1]
Since the aim of this paper is to propose a model for recognizing traffic generated by WebRTC communication, models have been created using classification algorithms based on machine learning
A comparative analysis of classification algorithms on the same dataset is presented in Table 3 in terms of six quality metrics: (i) True Positive Rate (TPR) indicating the number of correctly classified positive samples in relation to the total number of positive samples, (ii) False Positive Rate (FPR) indicating the number of incorrectly classified negative samples in relation to the total number of negative samples, (iii) Precision indicating the number of correctly classified positive samples in relation to the total number of samples classified as positive, (iv) Recall, (v) F-Measure, a measure of test’s accuracy, (vi) Receiver Operating Characteristic (ROC) Area

Summary

Introduction

Web Real-Time Communication (WebRTC) is an open-source project that enables direct real-time communication in web browsers and mobile applications through a simple Application Programming Interface (API) [1]. It contains the basic building blocks for highquality communication on the web, such as network, audio, and video components used in audio and video applications. Using existing protocols and applied APIs, WebRTC enables audio and video communication between users via a peer-to-peer connection, supporting all modern web browsers. Traffic classification was largely based on well-known protocol ports. WebRTC-based applications use random or non-standard ports, which makes these approaches much less efficient than in the past, as this mode of communication does not include any characteristic pattern for semantically based recognition [5] [6]

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