Learning from socio-economic characteristics of IP geo-locations for cybercrime prediction Online publication date: Wed, 12-Nov-2014
by Keivan Kianmehr; Negar Koochakzadeh
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 7, No. 1/2, 2012
Abstract: Cybercrime detection solutions have recently received increased attention. Predicting the cybercrime potentiality of a request received by a server can reduce the risk of cybercrime. In this paper, we present an alternative solution to the current intrusion detection systems in that the socio-economic characteristics of IP geo-locations of a request are used to predict its crime potentiality. The IP address of a request is used to exploit its socio-economic characteristics. Using the IP address of a request, the physical location, from where the request has been sent, is identified. Socio-economic attributes of people living in that area are collected. These characteristics can specify the seriousness of a cybercrime associated with a request. Classification algorithms can be used to build a prediction model. We have conducted a case study in which we built a prediction model using a set of socio-economic attributes. Our results show the applicability of the proposed model.
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