Encrypted social media traffic classification

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Network traffic packets carry data belonging to a variety of applications. Classification of traffic helps networkoperators to identify specific applications and protocols that exist in a network, which can be useful for manydifferent purposes such as network planning, application prioritization for QoS guarantees, and policydeployment for security control.Network traffic monitoring is becoming a necessity in network security. The packets of social media suchas Facebook, Twitter, WhatsApp, Snapchat, Instagram, Weibo and WeChat are encrypted. Therefore, analyzingand classification is a challenge. The social networks cover various types of data such as video, text, picturewhich should be considered in classification. Encryption protocols such as Internet Protocol Security (IPsec),TLS, SSH, Bit Torrent, and Skype protocols was also influenced by protocols that are the most commonly usedin research of encrypted traffic classification. Almost all the presented encryption protocols can be divided intotwo main phases: the initialization of the connection and the transport of encrypted data.The objective of this project is to develop machine learning algorithms for classification of social media inencrypted traffic.This project will pull together talents from Ryerson University and Solana Networks and take theadvantage of the resources of Ryerson including experienced researchers, energetic and goal-driven graduatestudents with a track record of excellence of Solana Networks in design and development of the machinelearning based classifier to jointly advance encrypted social media traffic classification and analysis.