Learning from socio-economic characteristics of IP geo-locations for cybercrime prediction

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.