Using Complex Networks for Mining Malicious Activities in a Collaborative Map


  • Carlos Caminha Unifor
  • Vasco Furtado Unifor



Complex Networks, Data Mining, Security on the Web


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 di?cult 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 typicallyno 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 andlimitations of our approach.


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Author Biographies

Carlos Caminha, Unifor

Carlos Caminha is Masters candidate in Applied Computer Science. He is copartner of Wikinova Solutions where he develops applications in Java and PHP since 2007 with emphasis and experience on Complex network, multi-agent systems and collaborative systems with maps.

Vasco Furtado, Unifor

Vasco Furtado is professor of computer science at University of Fortaleza (UNIFOR), Brazil where he also leads a team of researchers in the Knowledge Engineering group that studies agent-based simulation and agent’s explanation on the web.   He has coordinated and developed R&D projects on the law enforcement domain. Prof. Furtado holds a PhD in Informatique from the University of Aix-Marseille III, France in 1997. He were on sabbatical in the Knowledge Systems Laboratory at Stanford University, 2006-2007.




How to Cite

Caminha, C., & Furtado, V. (2012). Using Complex Networks for Mining Malicious Activities in a Collaborative Map. Journal of Information and Data Management, 3(3), 179.



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