Examinando por Autor "Carmona-Murillo, Javier"

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Bitrate Analysis in 5G Networks for Video Streaming Services Using L-Moment Ratio Diagrams
(IEEE, 2023) Ortega Aparicio, Juan Antonio; Cortés Polo, David; Carmona-Murillo, Javier; Chidean, Mihaela I.
Now a days, the expansion of wireless technologies and the development of new services have arisen new network requirements that completely change the way that network providers supply the resources to these new services. In this context, this work includes a novel analysis based on the L-moments statistical theory oriented to the understanding of the 5G networks, specially in the situation when video streaming applications are used. Our analysis involves computing and representing the L-moment ratio diagram of the bitrate of both uplink and downlink channel for three specific services, i.e. the download of a large file and Netflix and Amazon Prime video streaming. Obtained results show clear differences in the statistical behaviour for all services when uplink and downlink, mainly due the different objectives and characteristics of these links. Results also show significant differences between the Netflix and Amazon Prime video streaming services. However, interesting and surprising similarities have also been detected, specially between the Amazon Prime video streaming service and the download of a large file. Conclusions obtained in this work could be useful input to novel network management and resource allocation algorithms in the next generation networks, being this idea one of our future research lines.
Network Traffic Characterization Using L-moment Ratio Diagrams
(2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), 2019-10) Chidean, Mihaela I.; Carmona-Murillo, Javier; Jacobsen, Rune H.; Zhang, Qi
5G networks are facing to new challenges related to the growing traffic volume and service diversity. Some of the major concerns in this new scenario are the security and privacy issues required for a full technology adoption. Traffic characterization is a compound of strategies intended to define formally the behaviour and patterns in the Internet traffic. In this work, we propose the use of statistical features of network flows to characterize some of the most common attacks in the current networks through the L-moment ratio diagrams. Our work identify the parameters that can discriminate normal from malicious traffic. Moreover, our preliminary results show that this technique enables the differentiation of anomalies and can also identify several types of attack traffic.