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- W4309431119 endingPage "976" @default.
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- W4309431119 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." @default.
- W4309431119 created "2022-11-27" @default.
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- W4309431119 date "2022-11-21" @default.
- W4309431119 modified "2023-09-25" @default.
- W4309431119 title "Detection of Anomalies in the Call Flow of an Emergency Management Center" @default.
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- W4309431119 doi "https://doi.org/10.1007/978-3-031-21333-5_97" @default.
- W4309431119 hasPublicationYear "2022" @default.
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