Enhanced Modelling of Exfiltration Events in Sun Life Cybersecurity Data

Theft or loss of sensitive data is a growing concern for companies who may suffer losses of consumer confidence, market valuation and intellectual property when large amounts of data are stolen. In this research project we will use an enhanced “screen and review” approach to combating exfiltration in a large data set of activity logs within a large corporate network. We will create realistic simulations of data theft events that can be used as a basis for machine learning, and for the design and prototyping of a system where human experts work with AI algorithms to detect and prevent data theft. We will also build visualization and browsing tools that make it easier for people to judge whether or not observed data access activity indicates malicious intent. Our goal will continue to be the development of a state-of-the-art system for monitoring and reporting possible exfiltration events as they happen.

Faculty Supervisor:

Mark Chignell

Student:

Lu Wang;Miles Chung;Yuhong Alisha Yang

Partner:

Sun Life Financial

Discipline:

Engineering - mechanical

Sector:

Finance, insurance and business

University:

University of Toronto