PUE attack detection in CWSN using collaboration and learning behavior

Blesa Martínez, Javier; Romero Perales, Elena; Araujo Pinto, Álvaro y Nieto-Taladriz García, Octavio (2013). PUE attack detection in CWSN using collaboration and learning behavior. "International Journal of Distributed Sensor Networks", v. 2013 (n. 815959); pp. 1-8. ISSN 1550-1329. https://doi.org/10.1155/2013/815959.

Descripción

Título: PUE attack detection in CWSN using collaboration and learning behavior
Autor/es:
  • Blesa Martínez, Javier
  • Romero Perales, Elena
  • Araujo Pinto, Álvaro
  • Nieto-Taladriz García, Octavio
Tipo de Documento: Artículo
Título de Revista/Publicación: International Journal of Distributed Sensor Networks
Fecha: 2013
Volumen: 2013
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Cognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of networks manage critical applications and data. Moreover, the specific constraints of WSN make the problem even more critical. However, effective solutions have not been implemented yet. Among the specific attacks derived from new cognitive features, the one most studied is the Primary User Emulation (PUE) attack. This paper discusses a new approach, based on anomaly behavior detection and collaboration, to detect the PUE attack in CWSN scenarios. A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. The algorithm has been tested using a cognitive simulator that brings important results in this area. For example, the result shows that the number of collaborative nodes is the most important parameter in order to improve the PUE attack detection rates. If the 20% of the nodes collaborates, the PUE detection reaches the 98% with less than 1% of false positives.

Más información

ID de Registro: 29555
Identificador DC: http://oa.upm.es/29555/
Identificador OAI: oai:oa.upm.es:29555
Identificador DOI: 10.1155/2013/815959
URL Oficial: http://www.hindawi.com/journals/ijdsn/2013/815959/cta/
Depositado por: Memoria Investigacion
Depositado el: 20 Sep 2014 09:39
Ultima Modificación: 21 Abr 2016 23:57
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