Resumen

CHANCHI-GOLONDRINO, Gabriel Elías; OSPINA-ALARCON, Manuel Alejandro  y  MUNOZ-SANABRIA, Luis Freddy. Characterization of cybercrime in the department of Cundinamarca during the first half of 2021 through exploratory analysis and machine learning. Ing. compet. [online]. 2023, vol.25, n.1, e-20511760.  Epub 30-Dic-2022. ISSN 0123-3033.  https://doi.org/10.25100/iyc.v25i1.11760.

Considering the wide diffusion that data analytics has had in different application areas and considering the scarcity of specific datasets associated with cybercrime within open data strategies in Colombia, this article aims to characterize cybercrime in the department of Cundinamarca, through the use of exploratory analysis and machine learning techniques. The present research was developed through 4 methodological phases: data adequacy, exploratory data analysis, application of machine learning models and finally generation of value-added information. For the development of the proposed study, a dataset was formed from the dataset of 35,000 records published by the National Police in the open data portal of Colombia, which addresses high-impact crimes within the department of Cundinamarca and occurred during the first half of 2021. The cybercrime dataset has a total of 1513 records and includes attributes such as: day, quarter, municipality, area, victim, age and crime, so that at the exploratory analysis level, descriptive statistics methods were applied on the different attributes, while at the machine learning level, the association rules and clustering models were applied in order to determine respectively the relationship of the attributes with the type of crime, and the representative groups formed by relating the age with the type of crime and the municipality with the type of crime. The study developed allowed to demonstrate the usefulness and potential of data analytics techniques in the field of cybersecurity, in order to support decision making by the relevant authorities.

Palabras clave : Cybersecurity; cybercrime; exploratory analysis; machine learning.

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