Diseño de un candado Iot de seguridad logística con machine learning y geolocalizacion anti-spoofing

Abstract

CONDITION FOR PUBLICATION OF PROJECT. Security in the Ecuadorian supply chain is compromised by attacks that threaten both cargo integrity and the reliability of tracking systems. This project presents the design of an intelligent IoT lock aimed at ensuring logistics integrity and traceability by detecting physical anomalies and GPS spoofing. To achieve this, a functional prototype was developed integrating an ESP32 microcontroller, an MPU6050 inertial sensor, and a GPS module. Physical anomaly detection was implemented using a Random Forest Machine Learning model, trained with empirical data from real routes and simulated attacks. Additionally, an anti-spoofing algorithm based on inertial data fusion and dead reckoning was developed. The results from the experimental validation showed the system’s high effectiveness: the anomaly classifier achieved an overall accuracy of 99% without generating false alarms, while the anti-spoofing mechanism successfully identified fraudulent GPS signals. It is concluded that sensor fusion through an artificial intelligence model is a robust and viable strategy that offers a dual layer of protection against physical and digital threats in freight transport. Keywords: Logistics Security, Internet of Things, Machine Learning, GPS Spoofing