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Analysis to Predict Cybercrime Using Information Technology in a Globalized Environment

机译：在全球化环境中使用信息技术预测网络犯罪的分析

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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.
机译：如今，IT在信息密集型全球经济中发挥着关键作用。在全球范围内，计算机和电信设备在存储，检索，传输和处理数据方面的应用已变得越来越普遍。但是，这带来了信息安全方面的主要挑战。我们生活的这个“信息时代”提出了一个新的现实，那就是全球范围内网络犯罪的增加。由于网络犯罪在全球范围内的高速增长以及缺乏打击这些犯罪的全球条约，因此诞生了在网络空间实施针对此类犯罪的措施的需求。本文的主要目标是通过信息技术提供一种预测网络犯罪的模型。它使用了RapidMiner程序，其中包含不同的数据挖掘技术。在不同的数据挖掘技术中，使用了决策树技术。基本上，这种数据挖掘技术包括两个阶段，第一个学习阶段和第二个预测阶段。获得了“社区和犯罪”存储库，并使用该存储库执行了决策树技术。在第一阶段，事实证明，在所有分析的维度中，人口（每个社区的人口）相对于网络犯罪中的其他维度而言，是权重最大的维度。在第二阶段，应用数据挖掘来预防网络犯罪是可行的。在预测过程中建立的社区中，所有社区都以最小的可能性再次成为网络犯罪的受害者。

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