Abstract
The continuous expansion of cyberattacks and their high degree of professionalization generates a context of extreme risk in all sectors of society, both public and private. In this scenario, emergency management centers become a target that could lead to a high impact for citizens and for emergency coordination. This work focuses on the application of Machine Learning techniques for the early detection of possible Telephone Denial of Service or TDoS attacks, regardless of whether they use traditional telephone exchanges or VoIP. To do this, an extensive dataset with real data is here used for the training of the proposed models. In addition, the implementation in Jetson Nano type devices is proposed for the integration in the real context of emergency centers.
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Acknowledgements
This research has been possible thanks to the Canary Islands Emergency Coordinating Center 112, the Binter Chair in Cybersecurity and the Edosoft Chair in Cloud Computing and Artificial Intelligence.
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Rosa-Remedios, C., Caballero-Gil, P. (2023). Detection of Anomalies in the Call Flow of an Emergency Management Center. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_97
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DOI: https://doi.org/10.1007/978-3-031-21333-5_97
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