TechRank: A Network-Centrality Approach for Informed Cybersecurity-Investment

TL;DR

This paper introduces TechRank, a network-centrality method using a bi-partite graph and recursive algorithms to measure influence among cybersecurity entities, aiding informed investment decisions.

Contribution

It presents a novel influence measurement approach for cybersecurity entities using a bi-partite graph and recursive algorithms, enabling tailored investment strategies.

Findings

Enhanced influence measurement accuracy for cybersecurity entities

Supports more informed and strategic cybersecurity investments

Provides a foundation for customizable investment strategies

Abstract

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.