Title: Biocybersecurity and applications of predictive physiological modelling

Authors: Lucas Potter; Sachin Shetty; Saltuk Karahan; Xavier-Lewis Palmer

Addresses: School of Cybersecurity, Old Dominion University, 5115 Hampton Blvd., USA ' VMASC, Old Dominion University, 1030 University Blvd., Suffolk, VA, 23435, USA ' School of Cybersecurity, Old Dominion University, 5115 Hampton Blvd., Monarch Hall Rm: 2115C Norfolk, VA, USA ' School of Cybersecurity, Old Dominion University, 5115 Hampton Blvd., Monarch Hall Rm: 2115C Norfolk, VA, USA

Abstract: In the scientific world, models are useful abstractions and sets of rules that can be used to predict hypothetical events. One's first exposure to models is likely in primary science education - models of gravity or chemical interaction. As computational power increases, the availability of models for different purposes continues to grow. For instance, models of climate and weather are more accurate than before. This growth has also grown to encompass the field of medicine. There are now an increasing number of computational models that describe the physiology of a given patient with great accuracy and interaction. The development of these models is a boon to the medical training field and is typically the reason for most of the development of these models. These models could be used to design a customised, multivariate biological threat. This threat would be entirely hypothetical if the medical training models were a singular development. However, the independent rise of low-cost, semi-autonomous biological manipulators gives this hypothetical threat very practicable teeth to combine high-resolution computational data with designer bioagents to deliver the optimal biological agent for a threat. This paper attempts to spur conversation based on the exploration of this distinct possibility and scenarios derived from the ideas proposed within the models described below.

Keywords: biocybersecurity; cyberbiosecurity; predictive; physiological modelling; machine learning; deep learning; artificial intelligence; biodefense; models; bioweapons.

DOI: 10.1504/IJSSE.2024.139410

International Journal of System of Systems Engineering, 2024 Vol.14 No.4, pp.349 - 361

Received: 08 Sep 2022
Accepted: 05 Nov 2022
Published online: 02 Jul 2024 *

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