Correlating Intrusion Scenarios with an Unsupervised Learning Model

The increasing sophistication of distributed attacks on networked infrastructure has resulted in a requirement for tools capable of abstracting and alerting network managers of network status across multiple data sources. The basic objective of this project is to provide a framework for correlating information from multiple network sources into a cohesive picture of system status. As such, it is necessary to provide a model capable of correlating information from both spatial and temporal information sources. To this end, an unsupervised model will be investigated using hierarchical abstraction to integrate and summarize data from multiple sources. Moreover, the efficient training of such a system will be addressed through the use of appropriate active learning models.

Faculty Supervisor:

Dr. Nur Zincir-Heywood

Student:

Patrick LaRoche

Partner:

Telecom Applications Research Alliance (TARA)

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Dalhousie University