PUE attack detection in CWSNs using anomaly detection techniques

Blesa Martínez, Javier; Romero Perales, Elena; Rozas Cid, Alba y Araujo Pinto, Álvaro (2013). PUE attack detection in CWSNs using anomaly detection techniques. "Eurasip Journal on Wireless Communications and Networking", v. 2013 (n. 215); pp. 1-13. ISSN 1687-1499. https://doi.org/10.1186/1687-1499-2013-215.

Descripción

Título: PUE attack detection in CWSNs using anomaly detection techniques
Autor/es:
  • Blesa Martínez, Javier
  • Romero Perales, Elena
  • Rozas Cid, Alba
  • Araujo Pinto, Álvaro
Tipo de Documento: Artículo
Título de Revista/Publicación: Eurasip Journal on Wireless Communications and Networking
Fecha: Agosto 2013
Volumen: 2013
Materias:
Palabras Clave Informales: Primary user emulation; Security; Cognitive wireless sensor networks; Behavior; Collaborative systems
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, integrating 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 since these kinds of networks manage critical applications and data. The specific constraints of WSN make the problem even more critical, and effective solutions have not yet been implemented. Primary user emulation (PUE) attack is the most studied specific attack deriving from new cognitive features. This work discusses a new approach, based on anomaly behavior detection and collaboration, to detect the primary user emulation attack in CWSN scenarios. Two non-parametric algorithms, suitable for low-resource networks like CWSNs, have been used in this work: the cumulative sum and data clustering algorithms. The comparison is based on some characteristics such as detection delay, learning time, scalability, resources, and scenario dependency. The algorithms have been tested using a cognitive simulator that provides important results in this area. Both algorithms have shown to be valid in order to detect PUE attacks, reaching a detection rate of 99% and less than 1% of false positives using collaboration.

Más información

ID de Registro: 28990
Identificador DC: http://oa.upm.es/28990/
Identificador OAI: oai:oa.upm.es:28990
Identificador DOI: 10.1186/1687-1499-2013-215
URL Oficial: http://jwcn.eurasipjournals.com/content/2013/1/215
Depositado por: Memoria Investigacion
Depositado el: 07 Jun 2014 12:13
Ultima Modificación: 22 Sep 2014 11:43
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