Malicious Traffic Predictive Indicators in Content Delivery Networks: a Big Data Analytics Approach

Content Delivery Networks (CDNs) represent the up-to-date standard to transfer data through on-growing Internet. They are designed to manage traffic streams to avoid network problems. Despite the fact that CDNs attempt to satisfy security requirements (authentication, data privacy and integrity), they face rising innovative threats, observable in the cyber-space. The main objective of this project is to design, implement and test new methods to detect and prevent maliciousness in CDNs. We aim at building an alternative solution to classical Web Application Firewalls (WAFs). We intend to leverage new technologies based on the big data analytics using network traffic streams. The project objectives fall into the use of big data analytical framework to extract key features from CDNs’ logs to identify existing and new cyber-threats. Additionally, we intend to use the specificities of Telecom networks such as the availability of user IDs and flow control in EPC networks to further complete our approach. As being one of the key player in CDNs’ market, the partner organization has a high interest to integrate a data analytical approach to corroborate security in such networks.

Faculty Supervisor:

Mourad Debbabi


Amine Boukhtouta





Information and communications technologies


Concordia University



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