Open AccessRecognition of traffic generated by WebRTC communication
Author(s) -
Nadina Ajdinović,
Semiurkić,
Jasmina Baraković Husić,
Sabina Baraković
Publication year - 2021
Publication title -
science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2831-1043
pISSN - 2744-2527
DOI - 10.54327/set2021/v1.i1.8
Subject(s) - webrtc , c4.5 algorithm , computer science , web traffic , naive bayes classifier , traffic classification , machine learning , quality of service , traffic analysis , artificial intelligence , data mining , multimedia , computer network , world wide web , the internet , support vector machine
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.