Modelling IoT for Immunisation

Today's networks are very complex and it has become difficult to predict computer attacks. The Internet of Things networks are built up from a very large number of devices using a variety of applications, and that causes their attack surface to continuously increase. Therefore, limiting damage spread against both known and yet unknown attacks becomes a very important concern. Network immunisation is defined as the process of vaccines distribution to nodes in order to stop an infection that is spreading. Previous studies, especially in epidemiology, revealed numerous challenges in computing the necessary metrics to make an accurate decision, due to their computational intractability. Furthermore, the approaches used so far to model propagation in complex networks treat all nodes and links uniformly by basing all the metrics only on the existence of a link between two nodes. Nevertheless, beyond the existence of a link, many other properties of a node such as the users' behaviours play a significant role in the propagation of a virus. In this paper, we propose a new approach that aims at considering the intrinsic properties of the nodes to build a layered network that can support the design of more realistic propagation models. Then, from that we derive a compact view that eases decision-making processes, security analysis where less computation resources are required.