Protection of Critical Emergency Response Infrastructures through Machine Learning
Carlos Rosa-Remedios, Jezabel Molina‐Gil, Pino Caballero‐Gil · 2025
Critical Emergency Response Infrastructures are essential to supporting the goals of sustainable development, especially during crises or emergencies that disrupt sustainable progress. While they provide the framework that enables emergency response operations to be effective and resilient, Public Safety Answering Points (PSAP) are the operational frontline of emergency response. A PSAP is the communications center where emergency calls made by the public are received (e.g., by dialing 911 in the United States and Canada, 112 in Europe, or equivalent local emergency numbers).The increasing reliance on PSAPs to ensure the appropriate response of civil protection services requires the implementation of advanced mechanisms to protect these essential infrastructures against possible unauthorized increases in call volume, which have the potential to affect citizen assistance in critical situations. This study employs statistical and machine learning techniques to analyse call patterns in PSAPs to identify and mitigate the effect of potentially malicious non-emergency calls. By using data analysis methods and pattern recognition techniques, it is feasible to characterise genuine emergency calls and differentiate them from those made with the intention of overloading the system, thus ensuring that response times for real emergencies are not compromised. Starting with the generation of a synthetic dataset that emulates the most significant features of a Telephony Denial of Service, along with an extensive set of real data, this research follows a multidimensional approach by combining temporal-spatial analysis and machine learning algorithms to characterize the behavioral patterns of emergency calls, using Gaussian Mixture Model. The obtained results identify the Gaussian Mixture Model (GMM), a probabilistic model that assumes that data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters, as an optimal solution for this characterization.