Master’s Thesis Presentation • Systems and Networking • Soteria: An Approach for Detecting Multi-institution Attacks

Please note: This master’s thesis presentation will take place online.

Saif Zabarah, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Raouf Boutaba, Samer Al-Kiswany

We present Soteria, a data processing pipeline for detecting multi-institution attacks. Soteria uses a set of Machine Learning techniques to detect future attacks, predict their future targets, and ranks attacks based on their predicted severity. Our evaluation with real data from Canada wide networks shows that Soteria can predict future attacks with 95% recall rate, predict the next targets of an attack with 97% recall rate, and can detect attacks in the first 20% of their life span. Soteria is deployed in production at CANARIE Canada wide network that connects tens of Canadian academic institutions.