Analysis of Network Crimes with Data Mining

The spread of network crimes has increased the amount of data recorded from our criminal offenses and organizational burdens with a large amount of data. Each of these data contains a lot of messages and information. If these data are properly analyzed, they can help supervisors to identify and track, as well as anticipate and prevent crimes and offenses.

In this study, by analyzing and analyzing the relationships in the networks and crime variables based on the Fata police database of the country, various networks were created considering the variables considered in the network analysis. Also, by analyzing the relationship that occurred in each network from the point of view in the field of social network analysis, in each network, high-potential nodes were selected and the offenders identified as highly likely to commit a crime, as well as prediction of possible communications. In general, we can say that our research and analysis method is based on the behavior of a cast of a network that acts as an element and member of the network. It is worth mentioning that in the present study simulations and analyzes of soft The NodeXL (Excel Node) and utilities have been used.

In order to identify and predict crimes, the nodes with high specificity were ranked. These nodes have a high coefficient of selection as offenders in a crime after a crime with the same incident. Also, according to the centrality of proximity index, it can be said that the ranking of a node in this index is high. So this node is likely to commit a crime again. This finding could help policing agencies monitor the activities of a node, or the same offender