Cybersecurity Visualization

Cybersecurity Visualization

Cybersecurity Visualization

This project will develop and evaluate visualizations to coherently represent the rich semantics of the security policy domain in order to promote collaboration between managers and IT security specialists and to improve the deployment of digital security within organizations. Effective shared understanding by organization managers and IT security specialists is critical for the successful specification and implementation of digital security policies. When the collaboration is poor, ambiguities and misunderstandings may result in assigning less priority to security policies proposed by security experts of the organization. It may also unnecessarily hamper the smooth security implementation in the organization, and ultimately the organization is prone to cyber-attacks.
Semantically rich visualizations can bridge this critical gap in comprehension, by providing a shared representation through which managers and IT specialists can understand each other’s perspectives and work together to refine policies and their implementations to contain the perceived threats to the organization. Little previous research exists on the design of visualizations as a solution to the problem of effective collaboration in cybersecurity policies.
The project will take two innovative approaches to develop visualizations for collaboration on cybersecurity policies. (a) It will invent a novel visualization for security policy collaboration using a proven approach to the design of semantically rich graphical representations, known as Law Encoding Diagrams. (b) It will investigate the application of an existing graphical notation for systems specification, Constraints Diagrams, as a potential solution to the security policy collaboration problem. Contrasting the two different types of representations will provide insights to what kind of visualizations can address the cyber security collaboration problem, why such visualizations work, and what is the most effect type of visualization.


Lead Principal Investigator (LPI):

  • Dr.Noora Fetais, Qatar University

Principal Investigators (PI):

  • Dr.Khaled Khan, Qatar University
  • Dr Peter Cheng, Sussex University


  • Dr Armstrong Nhlabatsi, Qatar University


  • Ms Rachael Fernandez, Qatar University


  • none

Media & Dates


  • Started: 5th January 2020
  • Ends: 4th January 2023

Funding (if applicable)

Funded By:

  • National Priority Research Project (NPRP), project number NPRP12S-0115 -190002

Collaborating Institution(s)

  • Sussex University