Detecting false testimonies in reputation systems using self-organizing maps

Bankovic, Zorana; Vallejo López, Juan Carlos; Fraga Aydillo, David y Moya Fernández, José Manuel (2013). Detecting false testimonies in reputation systems using self-organizing maps. "Logic Journal of the IGPL", v. 21 (n. 4); pp. 549-559. ISSN 1367-0751. https://doi.org/10.1093/jigpal/jzs028.

Descripción

Título: Detecting false testimonies in reputation systems using self-organizing maps
Autor/es:
  • Bankovic, Zorana
  • Vallejo López, Juan Carlos
  • Fraga Aydillo, David
  • Moya Fernández, José Manuel
Tipo de Documento: Artículo
Título de Revista/Publicación: Logic Journal of the IGPL
Fecha: Agosto 2013
Volumen: 21
Materias:
Palabras Clave Informales: Reputation systems, bad mouthing, ballot stuffing, self-organizing maps
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

It has been demonstrated that rating trust and reputation of individual nodes is an effective approach in distributed environments in order to improve security, support decision-making and promote node collaboration. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies. In one scenario, the attackers collude to give negative feedback on the victim in order to lower or destroy its reputation. This attack is known as bad mouthing attack. In another scenario, a number of entities agree to give positive feedback on an entity (often with adversarial intentions). This attack is known as ballot stuffing. Both attack types can significantly deteriorate the performances of the network. The existing solutions for coping with these attacks are mainly concentrated on prevention techniques. In this work, we propose a solution that detects and isolates the abovementioned attackers, impeding them in this way to further spread their malicious activity. The approach is based on detecting outliers using clustering, in this case self-organizing maps. An important advantage of this approach is that we have no restrictions on training data, and thus there is no need for any data pre-processing. Testing results demonstrate the capability of the approach in detecting both bad mouthing and ballot stuffing attack in various scenarios.

Más información

ID de Registro: 28977
Identificador DC: http://oa.upm.es/28977/
Identificador OAI: oai:oa.upm.es:28977
Identificador DOI: 10.1093/jigpal/jzs028
URL Oficial: http://jigpal.oxfordjournals.org/content/21/4/549.short
Depositado por: Memoria Investigacion
Depositado el: 04 Jun 2014 16:35
Ultima Modificación: 22 Sep 2014 11:43
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