Data-Driven Cyber Threat Analysis for Cyber Security

Today, businesses observe change in priorities, and data intelligence becomes one of the key drivers for this change. Organizations are generating non-financial data from existing systems and applications. It is necessary to analyze this data and undertake necessary measure to prevent the data from any cyber-attack.

Cyber threat modelling and analysis is an active research area and industries are constantly looking for effective analysis techniques to understand the threat landscape so that appropriate actions can be taken. Thus, building a threat model for organization systems and data will provide decision maker with long last strategy and focus understanding of their security posture and eventually saving cost. Among the existing threat models and standards, there is a lack of focus on understanding threats relating to non-financial data which could pose any potential cyber risk to the business.

This research contributes for a data-driven threat model and aims to tackle cyber risks from the data. The model focuses on the three levels of data abstraction, i.e., control, management and business along with data states, i.e., rest, process, and transit. Management data relates with device management, whereas control data focuses on device functions. Finally, business data is also known as forwarding data deals with underlying actions on control data. The model reveals risks imposed to systems interacted with data, unlike other models which use critical systems as a reference. Additionally, the abstraction levels empower the model with dynamic adoption of system changes and help to align the scope of threats and countermeasure tools.