Dynamic Bayesian Networks, Elicitation and Data Embedding for Secure Environments
Kieran Drury, Jim Q. Smith

TL;DR
This paper introduces a formal protocol using dynamic Bayesian networks to securely translate and embed models for real-time decision support in police environments, addressing data confidentiality and missingness.
Contribution
It presents a novel protocol for securely translating academic models into police-use models and demonstrates building libraries for real-time decision support in secure environments.
Findings
Secure model translation protocol developed
Libraries enable real-time decision support
Application to vehicle attack scenario demonstrated
Abstract
Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities can remain undisclosed. Data informing an ongoing incident is often sparse, with a large proportion of relevant data only coming to light after the incident culminates or after police intervene - by which point it is too late to make use of the data to aid real-time decision making for the incident in question. Much of the data that is available to police to support real-time decision making is highly confidential so cannot be shared with academics, and is therefore missing to them. In this paper, we describe the development of a formal protocol where a graphical model is used as a framework for securely translating a model designed by an academic team to a model for use by a police team. We then show, for the first time, how libraries of these models can be built…
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Taxonomy
TopicsNetwork Security and Intrusion Detection
