Investigate machine learning algorithms to develop anomaly detection methods

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:

Dr. Rabab K. Ward

Student:

Xin Yi Yong

Partner:

Metafor Software

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

University of British Columbia