Reference number : GC-2017-Q4-01278, amendment number : , agreement type : G, recipient legal name : Mohammadi, Farah (Ryerson University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Network traffic packets carry data belonging to a variety of applications. Classification of traffic helps network_x000D_
operators to identify specific applications and protocols that exist in a network, which can be useful for many_x000D_
different purposes such as network planning, application prioritization for QoS guarantees, and policy_x000D_
deployment for security control._x000D_
Network traffic monitoring is becoming a necessity in network security. The packets of social media such_x000D_
as Facebook, Twitter, WhatsApp, Snapchat, Instagram, Weibo and WeChat are encrypted. Therefore, analyzing_x000D_
and classification is a challenge. The social networks cover various types of data such as video, text, picture_x000D_
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 used_x000D_
in research of encrypted traffic classification. Almost all the presented encryption protocols can be divided into_x000D_
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 in_x000D_
encrypted traffic._x000D_
This project will pull together talents from Ryerson University and Solana Networks and take the_x000D_
advantage of the resources of Ryerson including experienced researchers, energetic and goal-driven graduate_x000D_
students with a track record of excellence of Solana Networks in design and development of the machine_x000D_
learning based classifier to jointly advance encrypted social media traffic classification and analysis., program purpose french : Network traffic packets carry data belonging to a variety of applications. Classification of traffic helps network_x000D_
operators to identify specific applications and protocols that exist in a network, which can be useful for many_x000D_
different purposes such as network planning, application prioritization for QoS guarantees, and policy_x000D_
deployment for security control._x000D_
Network traffic monitoring is becoming a necessity in network security. The packets of social media such_x000D_
as Facebook, Twitter, WhatsApp, Snapchat, Instagram, Weibo and WeChat are encrypted. Therefore, analyzing_x000D_
and classification is a challenge. The social networks cover various types of data such as video, text, picture_x000D_
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 used_x000D_
in research of encrypted traffic classification. Almost all the presented encryption protocols can be divided into_x000D_
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 in_x000D_
encrypted traffic._x000D_
This project will pull together talents from Ryerson University and Solana Networks and take the_x000D_
advantage of the resources of Ryerson including experienced researchers, energetic and goal-driven graduate_x000D_
students with a track record of excellence of Solana Networks in design and development of the machine_x000D_
learning based classifier to jointly advance encrypted social media traffic classification and analysis., agreement title english : Encrypted social media traffic classification, agreement title french : Encrypted social media traffic classification, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada