Analysis to predict cybercrime using information technology in a globalized environment

Today, IT plays a key role in the information-intensive global economy. The application of computers and telecommunications equipment to store, retrieve, transmit and manipulate data has become widespread in a global context. However, this has brought with its major challenges in information security. A new reality was addressed in this 'Information Age' in which we live, which is the increase of cybercrime on a global level. Due to the high growth of cybercrime on a global scale and the lack of a global treaty to combat these crimes, the need to implement measures against these types of crimes in cyberspace was born. The main objective of this text is through information technologies to provide a model for predicting cybercrime. It used the program RapidMiner which contains different data mining techniques. Within the different data mining techniques, the decision tree technique was used. Basically, this data mining technique consists of two stages, the first learning stage and the second prediction stage. The repository 'communities and crimes' was obtained, with which the decision tree technique was carried out. In the first stage it turned out that, among all the dimensions analyzed, popilation (population per community) turned out to be the dimension with the greatest weight with respect to the other dimensions in cybercrime. In the second stage it was feasible to apply data mining to prevent cybercrime. Among the communities established in the prediction process, all resulted with a minimum probability of being victims of cybercrime again.