Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Network traffic packets carry data belonging to a variety of applications. Classification of traffic helps networkx000D
operators to identify specific applications and protocols that exist in a network, which can be useful for manyx000D
different purposes such as network planning, application prioritization for QoS guarantees, and policyx000D
deployment for security control.x000D
Network traffic monitoring is becoming a necessity in network security. The packets of social media suchx000D
as Facebook, Twitter, WhatsApp, Snapchat, Instagram, Weibo and WeChat are encrypted. Therefore, analyzingx000D
and classification is a challenge. The social networks cover various types of data such as video, text, picturex000D
which should be considered in classification. Encryption protocols such as Internet Protocol Security (IPsec),x000D
TLS, SSH, Bit Torrent, and Skype protocols was also influenced by protocols that are the most commonly usedx000D
in research of encrypted traffic classification. Almost all the presented encryption protocols can be divided intox000D
two main phases: the initialization of the connection and the transport of encrypted data.x000D
The objective of this project is to develop machine learning algorithms for classification of social media inx000D
encrypted traffic.x000D
This project will pull together talents from Ryerson University and Solana Networks and take thex000D
advantage of the resources of Ryerson including experienced researchers, energetic and goal-driven graduatex000D
students with a track record of excellence of Solana Networks in design and development of the machinex000D
learning based classifier to jointly advance encrypted social media traffic classification and analysis.