TechRank: A Network-Centrality Approach for Informed Cybersecurity-Investment
This provides a method for decision-makers in cybersecurity to make more informed technological investments, though it appears incremental as it adapts existing network-centrality approaches to this specific domain.
The paper tackles the problem of measuring mutual influence between companies and technologies in the cybersecurity ecosystem using a bi-partite graph with weighted nodes via a recursive reflection-based algorithm, resulting in more precise influence measurements to inform investment strategies.
The cybersecurity technological landscape is a complex ecosystem in which entities -- such as companies and technologies -- influence each other in a non-trivial manner. Measuring the influence between entities is a tenet for informed technological investments in critical infrastructure. To study the mutual influence of companies and technologies from the cybersecurity field, we consider a bi-partite graph that links both sets of entities. Each node in this graph is weighted by applying a recursive algorithm based on the method of reflection. This endeavor helps to measure the impact of an entity on the cybersecurity market. Our results help researchers measure more precisely the magnitude of influence of each entity, and allows decision-makers to devise more informed investment strategies, according to their portfolio preferences. Finally, a research agenda is suggested, with the aim of allowing tailor-made investments by arbitrarily calibrating specific features of both types of entities.