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Communities in RUIdeRA
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Recent Submissions
Item
MintEDGE: Multi-tier sImulator for eNergy-aware sTrategies in Edge Computing
(Association for Computing Machinery, 2023) Gómez Mora, Blas; Bayhan, Suzan; Coronado Calero, Estefanía; Villalón Millán, José Miguel; Garrido del Solo, Antonio José
Edge computing has transformed cellular networks, offering fast response times by moving computing resources to
the network’s edge. This not only reduces the burden on the
Wide Area Network (WAN) but also enables latency-sensitive
applications. However, the widespread deployment of edge
computing raises concerns regarding its sustainability. In this
work, we present MintEDGE, a simulation framework that
models a fully configurable edge-enabled cellular network.
MintEDGE empowers researchers and practitioners to design and assess energy-saving strategies for edge computing.
We discuss the details of the simulator and its customizable
elements like user mobility, the possibility to use predictive
workload algorithms, and diverse application scenarios at
scale. MintEDGE is released under a permissive MIT license
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Automatizando las investigaciones forenses en entornos IoT mediante el análisis del tráfico de red en tiempo real
(Universidade de Vigo, 2023-06) Ruiz Villafranca, Sergio; Castelo Gómez, Juan; Roldán Gómez, José
El campo del analisis forense ha visto como la ´
irrupcion del Internet de las Cosas (IoT) ha tra ´ ´ıdo consigo
grandes retos a la hora de enfocar la manera de llevar a cabo
las investigaciones forenses. Su heterogeneidad, la cantidad de
dispositivos que encontramos en el, la volatilidad de los datos ´
intercambiados, y la dificultad de poder interactuar f´ısicamente
con los dispositivos hace que procesos claves de un analisis ´
necesiten de nuevas tecnicas que se ajusten a los requisitos del ´
IoT. Ante esta tesitura, este art´ıculo presenta un metodo para ´
la automatizacion de los procesos de identificaci ´ on, adquisici ´ on, ´
y analisis de fuentes de evidencia empleando un nodo edge ´
capaz de estudiar los paquetes intercambiados en tiempo real
en una red usando los protocolos IoT mas comunes. Haciendo ´
uso de esta informacion, se proporciona una herramienta que ´
permite ejecutar las acciones correspondientes para asegurar la
correcta iniciacion del proceso forense. La funcionalidad de esta ´
propuesta es evaluada en un entorno IoT industrial, mostrando
el funcionamiento de los distintos servicios implementados y
demostrando que es util para ser utilizada como herramienta ´
forense.
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MECInOT: a multi‑access edge computing and industrial internet of things emulator for the modelling and study of cybersecurity threats
(Springer, 2023) Ruiz Villafranca, Sergio; Carrillo Mondéjar, Javier; Castelo Gómez, Juan Manuel; Roldán Gómez, José
In recent years, the Industrial Internet of Things (IIoT) has grown rapidly, a fact that
has led to an increase in the number of cyberattacks that target this environment
and the technologies that it brings together. Unfortunately, when it comes to using
tools for stopping such attacks, it can be noticed that there are inherent weaknesses
in this paradigm, such as limitations in computational capacity, memory and network
bandwidth. Under these circumstances, the solutions used until now in conventional
scenarios cannot be directly adopted by the IIoT, and so it is necessary to
develop and design new ones that can effectively tackle this problem. Furthermore,
these new solutions must be tested in order to verify their performance and viability,
which requires testing architectures that are compatible with newly introduced IIoT
topologies. With the aim of addressing these issues, this work proposes MECInOT,
which is an architecture based on openLEON and capable of generating test scenarios
for the IIoT environment. The performance of this architecture is validated by
creating an intelligent threat detector based on tree-based algorithms, such as decision
tree, random forest and other machine learning techniques. Which allows us to
generate an intelligent and to demonstrate, we could generate an intelligent threat
detector and demonstrate the suitability of our architecture for testing solutions in
IIoT environments. In addition, by using MECInOT, we compare the performance of
the different machine learning algorithms in an IIoT network. Firstly, we present the
benefits of our proposal, and secondly, we describe the emulation of an IIoT environment
while ensuring the repeatability of the experiments.
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A MEC-IIoT intelligent threat detector based on machine learning boosted tree algorithms
(Elsevier, 2023-06) Ruiz Villafranca, Sergio; Roldán Gómez, José; Carrillo Mondéjar, Javier; Castelo Gómez, Juan Manuel; Villalón Millán, José Miguel
In recent years, new management methods have appeared that mark the beginning of a new industrial
revolution called Industry 4.0 or the Industrial Internet of Things (IIoT). IIoT brings together new emerging
technologies, such as the Internet of Things (IoT), Deep Learning (DL) and Machine Learning (ML), that
contribute to new applications, industrial processes and efficiency management in factories. This combination
of new technologies and contexts is paired with Multi-access Edge Computing (MEC) to reduce costs through
the virtualisation of networks and services. As these new paradigms increase in growth, so does the number
of threats and vulnerabilities, making IIoT a very desirable target for cybercriminals. In addition, IIoT devices
have certain intrinsic limitations, especially due to their limited resources, and this makes it impossible, in
many cases, to detect attacks by using solutions designed for other paradigms. So it is necessary to design,
implement and evaluate new solutions or adapt existing ones. Therefore, this paper proposes an intelligent
threat detector based on boosted tree algorithms. Such detectors have been implemented and evaluated in an
environment specifically designed to test IIoT deployments. In this way, we can learn how these algorithms,
which have been successful in multiple contexts, behave in a paradigm with known constraints. The results
obtained in the study show that our intelligent threat detector achieves a mean efficiency of between 95%–99%
in the F1 Score metric, indicating that it is a good option for implementation in these scenarios.
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Aportación al conocimiento de la creación y funcionamiento de bandas de música en la comarca histórica del Campo de Calatrava (Ciudad Real). El caso de Mestanza y la figura del maestro-director
(Universidad de Castilla-La Mancha, 2023) Vallejo Climent, Javier Vicente
Es habitual que, en general, el público aficionado a la Música, e incluso el alumnado de los Conservatorios, se haya preocupado siempre por conocer los grandes períodos históricos de la música, sus características, instrumentos y agrupaciones, así como los grandes compositores que han protagonizado la evolución musical a lo largo de los tiempos. Ello tiene su lógica, pero, a veces, pasa desapercibido el conocimiento de artistas más modestos que se esforzaron por engrandecer el arte de los sonidos, aportando una dosis de felicidad a las personas de su entorno, a veces en pequeños pueblos. En muchas ocasiones, quizá por haberlos tenido tan cerca de nosotros, su labor puede habernos pasado desapercibida, y lo mismo puede haber ocurrido con aquellas agrupaciones modestas como una rondalla, un grupo coral o una banda de música a las cuales pudieron pertenecer. Por ello, creemos que puede tener interés dar a conocer su labor.