Aplicacion de analitica de datos para la deteccion de anomalias y fortalecimiento de la seguridad en la red WiFi del campus universitario de la Universidad Nacional del Altiplano

In today's university environment, wireless connectivity is an essential resource for academic, administrative, and research activities. However, at the National University of the Altiplano of Puno (UNAP), the use of a QR code access system on the institutional Wi-Fi network has generated vulnerabilities related to the lack of individual authentication, user traceability, and access control. Given this situation, this study aims to strengthen the security of the university's wireless network through the application of data analytics, employing descriptive, predictive, and prescriptive approaches to the logs generated by the wireless controller (WLC). The methodology consisted of collecting and processing connection data from users, devices, and daily traffic, analyzing behavioral patterns, and detecting anomalies based on statistical models and machine learning algorithms. The results revealed critical usage peaks between 10:00 and 14:00, as well as anomalous behavior associated with recurring devices and irregular traffic spikes. This allowed for the establishment of dynamic alert thresholds and recommendations for improvements in bandwidth management and authentication. Furthermore, the conclusion states that integrating advanced analytics into the management of university networks not only identifies vulnerabilities and optimizes WiFi service performance, but also advances towards an intelligent, proactive infrastructure aligned with modern institutional cybersecurity standards.