Investigate machine learning algorithms to detect anomalies incomputing infrastructures in real-time

Metafor is developing a new class of IT system management solution to monitor computer and application activities, and alert when anomalous behavior occurs. Current commercial tools for anomaly detection use simple statistical rules and thresholds to detect anomalies. These methods are failing for today’s dynamic cloud environment where change is constant. As a result, IT operators are flooded with false alerts; become overwhelmed with alert fatigue and learn to ignore the alerts. This research internship will help to identify and create appropriate anomaly detection algorithms for the IT environment and extend and improve the functionality of Metafor’s current suite of algorithms.

Intern: 
Hongyang Zhang
Superviseur universitaire: 
Dr. Ruben Zamar
Project Year: 
2015
Province: 
British Columbia
Partenaire: 
Programme: