Improving the Accuracy of Data Loss Prevention Systems

Scotiabank employs teams of cybersecurity specialists across its global operations and partners with a variety of external organizations to prevent and investigate any electronic attempts to gain access to the Bank’s data. At the same time, employees are continuously educated and expected to look for warning signs and efforts to infiltrate that data as well. Currently, Scotiabank’s Data Loss Prevention (DLP) systems have a high false positive rate in identifying data breaches and cyber-attacks, which require significant manual intervention. The project will use machine learning algorithms, data mining principles, and cybersecurity threat modelling to improve the accuracy of Scotiabank’s DLP systems by reducing the false positive rate. We will also develop automated reporting systems and reduce the need for manual verification of data loss events. This project will benefit the partner by improving upon the accuracy and automation of their DLP systems, as well as benefiting Canadian consumers who rely on Scotiabank to keep their personal data safe.

Faculty Supervisor:

Hasan Cavusoglu

Student:

Xiao Peng Song

Partner:

Scotiabank

Discipline:

Business

Sector:

University:

University of British Columbia

Program: