Understanding cybersecurity data and analysis models with intelligent visualization

Cybersecurity incidents such as data breaches have become more critical and costly than ever, as we are generating, processing, and relying on more digital information every day. To quickly identify potential security attacks and prevent them in a sea of system activities, machine learning (ML) has been applied to support security analysts’ decisions. However, the task is challenging as cybersecurity data is often large, noisy, and complex, and there lack sufficient ground truth. Further, ML models are often a black box to the analysts, which usually produce results not easily interpretable. Due to such lack of understanding, ML deficiency largely impact customers’ experience and business operations. This research will design, develop, and evaluate a suite of intelligent visualization tools and analytical pipelines that allow security analysts and model developers to effectively explore large cybersecurity data, identify and investigate security attacks, as well as understand security analysis models and their predictions.

Faculty Supervisor:

Jian Zhao

Student:

Partner:

BlackBerry (Waterloo, ON)

Discipline:

Computer science

Sector:

Information and cultural industries; Manufacturing

University:

University of Waterloo

Program: