We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Caracterización de delitos cibernéticos del departamento de Cundinamarca durante el primer semestre de 2021 mediante análisis exploratorio y aprendizaje de máquina.
- Authors
Elías Chanchí-Golondrino, Gabriel; Alejandro Ospina-Alarcón, Manuel; Freddy Muñoz-Sanabria, Luis
- Abstract
Taking into account 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.
- Subjects
CUNDINAMARCA (Colombia : Dept.); COLOMBIA; DESCRIPTIVE statistics; DECISION making; DATA analysis; INTERNET security; CRIME; CRIME statistics; MACHINE learning
- Publication
Ingeniería y Competitividad, 2023, Vol 25, Issue 1, p2
- ISSN
0123-3033
- Publication type
Article
- DOI
10.25100/iyc.v25i1.11760