| Title | Conceptual Modelling of Criticality of Critical Infrastructure Nth Order Dependency Effect Using Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Mbanaso, U. M., Makinde, J. A. |
| Conference Name | 2020 IEEE 2nd International Conference on Cyberspac (CYBER NIGERIA) |
| Keywords | Analytical models, Artificial neural networks, complex networks, Complexity theory, composability, compositionality, Conferences, critical information infrastructure protection, critical infrastructure, Cyber Dependencies, degree of infrastructure criticality degree of infrastructure dependency, Human Behavior, Indexes, Metrics, pubcrawl, Recurrent neural networks, resilience, Resiliency, Scalability |
| Abstract | This paper presents conceptual modelling of the criticality of critical infrastructure (CI) nth order dependency effect using neural networks. Incidentally, critical infrastructures are usually not stand-alone, they are mostly interconnected in some way thereby creating a complex network of infrastructures that depend on each other. The relationships between these infrastructures can be either unidirectional or bidirectional with possible cascading or escalating effect. Moreover, the dependency relationships can take an nth order, meaning that a failure or disruption in one infrastructure can cascade to nth interconnected infrastructure. The nth-order dependency and criticality problems depict a sequential characteristic, which can result in chronological cyber effects. Consequently, quantifying the criticality of infrastructure demands that the impact of its failure or disruption on other interconnected infrastructures be measured effectively. To understand the complex relational behaviour of nth order relationships between infrastructures, we model the behaviour of nth order dependency using Neural Network (NN) to analyse the degree of dependency and criticality of the dependent infrastructure. The outcome, which is to quantify the Criticality Index Factor (CIF) of a particular infrastructure as a measure of its risk factor can facilitate a collective response in the event of failure or disruption. Using our novel NN approach, a comparative view of CIFs of infrastructures or organisations can provide an efficient mechanism for Critical Information Infrastructure Protection and resilience (CIIPR) in a more coordinated and harmonised way nationally. Our model demonstrates the capability to measure and establish the degree of dependency (or interdependency) and criticality of CIs as a criterion for a proactive CIIPR. |
| DOI | 10.1109/CYBERNIGERIA51635.2021.9428861 |
| Citation Key | mbanaso_conceptual_2021 |
Conceptual Modelling of Criticality of Critical Infrastructure Nth Order Dependency Effect Using Neural Networks