Investigate parametric machine learningalgorithms to develop anomaly detectionmethods on real-time data

The industry partner, Metafor is developing a new class of IT system management solution. As part of this project, Metafor is building a product feature that monitors computer and network activities and looks for signs of anomalies. This is an important problem as anomalies are usually associated with abnormal user or system behaviors that can potentially result in problems such as system breakdown. As the properties of anomalies and normal behaviours are stochastic and dynamic by nature, efficient and intelligent signal processing and machine learning algorithms are required to detect these anomalies. In this project, the intern will do a comprehensive survey on the state-of-the art of real-time anomaly detection; investigate a set of system indicators or features as well as machine learning algorithms that can potentially be useful in detecting anomalies.

Faculty Supervisor:

Paul Gustafson

Student:

Partner:

Metafor Software

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

The University of British Columbia

Program: