Using Complex Networks for Mining Malicious Activities in a Collaborative Map

Abstract

Collaboration with content sharing via digital maps is a type of application that is characteristic of the context of the social web. A malicious activity that is dicult to detect in this interactive context is the generation of a false trend on the map as the result of a plot in which several false reports by more than one person are done. In this paper, we describe how modeling complex networks of crime reported on a collaborative (or crowd) map can help identify regularities, and therefore show deviations arising from malicious activity. The idea here is to model a network comprised of users who reported crimes and the locations where such crimes were reported (e.g.: a census tract). Starting from a bipartite network model in which the vertices are individuals and census tracts, we projected a monopartite network of users in which the edges indicate the strength of connection between them. This connection strength indicates the degree of co-relatedness of the reports of crime made by these two users in a particular place. By characterizing this, we were able to observe that the relationships of non-hub users among themselves are typically no stronger than the relationship between such non-hub users and the hubs. If this happens, the evidence of malicious activity becomes clear. Simulation of malicious activities in this dataset has allowed evaluating the contributions and limitations of our approach.