Predicting exposure to risk for active tasks
Prior research has been considering how we might better understand and predict the consequences of cyber-attacks based on knowledge of the business processes, people and tasks and how they utilise the information infrastructure / digital assets that might be exposed to specific attack vectors. However, this can clearly be refined by moving to an understanding of those tasks live or active at the time of an attack propagating across a system. If this can be calculated, then an accurate model of where risk may manifest and the harm that may result can be constructed. This project would explore the potential for such a model through practical experimentation and development of software monitors to be placed on a network aimed at inferring the tasks and users that are active based from network traffic. If time allows then host-based sensors might also be explored (such as on an application server) to further refine the understanding of which users and live on which applications etc.
Requirements: Students must be able to construct software prototypes and have a working knowledge of network architectures and computer systems.