Abstract: A system including at least one processor is configured for executing a mathematical contagion model based on percolation theory to anticipate the loss distribution resulting from a cyberattack on a class of client-server network architectures with K different client types. The system includes a framework that computes the exact mean and variance of the losses depending on key parameters such as probabilities of attack types, the topology of the network of clients, and contagion strength among the clients. This framework can be used by insurance companies to estimate the liability assessments of insuring a given IT network against the damages that may arise from cyberattacks, including the losses of company revenue, and improved decision-making in cybersecurity protection investments of the network. Further, the framework provides insights into better investment strategies for cybersecurity protection on the client-server network.

Abstract: A computing system includes a processor having access to data associated with a plurality of devices of a network. The processor is configured to leverage the data to execute a loss model that estimates cyber risk loss distribution of the network associated with healthcare environment and infrastructure using bond percolation on a mixed random graphs approach.

Abstract: Aspects of the present disclosure relate to implementation of a dynamical structural percolation model, that may be implemented via at least one computing device, and models the loss distribution for cyber risk due to breach of IT networks of a single small or medium-sized enterprise, which is believed to be a technical improvement responsive to the foregoing issues and challenges with known risk/loss distribution models.

Abstract: Aspects of the present disclosure relate to implementation of a dynamical structural percolation model, that may be implemented via at least one computing device, and models the loss distribution for cyber risk due to breach of IT networks of a single small or medium-sized enterprise, which is believed to be a technical improvement responsive to the foregoing issues and challenges with known risk/loss distribution models.