PUE attack detection in CWSNs using anomaly detection techniques

Blesa Martínez, Javier and Romero Perales, Elena and Rozas Cid, Alba and 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.

Description

Title: PUE attack detection in CWSNs using anomaly detection techniques
Author/s:
  • Blesa Martínez, Javier
  • Romero Perales, Elena
  • Rozas Cid, Alba
  • Araujo Pinto, Álvaro
Item Type: Article
Título de Revista/Publicación: Eurasip Journal on Wireless Communications and Networking
Date: August 2013
Volume: 2013
Subjects:
Freetext Keywords: Primary user emulation; Security; Cognitive wireless sensor networks; Behavior; Collaborative systems
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (799kB)

Abstract

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.

More information

Item ID: 28990
DC Identifier: http://oa.upm.es/28990/
OAI Identifier: oai:oa.upm.es:28990
DOI: 10.1186/1687-1499-2013-215
Official URL: http://jwcn.eurasipjournals.com/content/2013/1/215
Deposited by: Memoria Investigacion
Deposited on: 07 Jun 2014 12:13
Last Modified: 22 Sep 2014 11:43
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM