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

TL;DR

This study applies data analytics and machine learning to university Wi-Fi logs to detect anomalies, improve security, and optimize network performance, addressing vulnerabilities caused by a QR code access system.

Contribution

It introduces a comprehensive data analytics approach combining statistical and machine learning methods to enhance Wi-Fi security and management in a university setting.

Findings

Identification of peak usage times and anomalous behaviors

Detection of irregular traffic spikes and recurring devices

Recommendations for bandwidth and authentication improvements

Abstract

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.