In this paper, we provide a model that aims to describe the impact of a massive cyber attack on an insurance portfolio, taking into account the structure of the network. Due to the contagion, such an event can rapidly generate consequent damages, and mutualization of the losses may not hold anymore. The composition of the portfolio should therefore be diversified enough to prevent or reduce the impact of such events, with the difficulty that the relationships between actor is difficult to assess. Our approach consists in introducing a multi-group epidemiological model which, apart from its ability to describe the intensity of connections between actors, can be calibrated from a relatively small amount of data, and through fast numerical procedures. We show how this model can be used to generate reasonable scenarios of cyber events, and investigate the response to different types of attacks or behavior of the actors, allowing to quantify the benefit of an efficient prevention policy.