Investigate machine learning algorithms todevelop anomaly detection methods on real-timedata: Non-parametric approaches

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. Finally, the intern will implement
suitable algorithms to predict the presence of anomalies in the system in real time.

Faculty Supervisor:

Rabab Ward

Student:

Partner:

Metafor Software

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

The University of British Columbia

Program: