Identifying critical nodes in multi-layered networks under multi-vector malware attack

Computer viruses are evolving by developing multiple spreading mechanisms that are simultaneously used during the infection process. The identification of the nodes that allow a better spreading efficiency of these kind of viruses is becoming a determinant part of the defensive strategy against malware. These multi-vector viruses can be modeled in multi-layered networks in which each node belongs simultaneously to different layers, adapting the spreading vector to the properties of the layer. This way, the same virus has different propagation rates in each layer and also in the multi-layered network considered as a whole. The set of nodes selected as initial group of infected subjects can determine the final propagation of the infection. In this work, we analyze the spreading of a virus in a multi-layered network formed by M layers, given different sets of initial infected nodes, and, in particular, the effect of the initial selection on the efficiency of the infection. The initial group of infected nodes is selected according to properties of the nodes considered as part of a layer and also of the whole system. As an example, we apply this study to a multi-layered network formed by two layers: the social network of collaboration of the Spanish scientific communit