The detection of encrypted real-time traffic, both streaming and conversational, is an increasingly important issue for agencies in charge of lawful interception. Aside from well established technologies used in real-time communication (e.g. Skype, Facetime, Lync etc.) a new one is recently spreading: Web Real-Time Communication (WebRTC), which, with the support of a robust encryption method such as DTLS, offers capabilities for encrypted voice and video without the need of installing a specific application but using a common browser, like Chrome, Firefox or Opera. Encrypted WebRTC traffic cannot be recognized through methods of semantic recognition since it does not exhibit a discernible sequence of information pieces and hence statistical recognition methods are called for. In this paper we propose and evaluate a decision theory based system allowing to recognize encrypted WebRTC traffic by means of an open-source machine learning environment: Weka. Besides, a reasoned comparison among some of the most credited algorithms (J48, Simple Cart, Naive Bayes, Random Forests) in the field of decision systems has been carried out, indicating the prevalence of Random Forests.
['Mario Di Mauro', 'Maurizio Longo']
Revealing encrypted WebRTC traffic via machine learning tools