RECLAMO: virtual and collaborative honeynets based on trust management and autonomous systems applied to intrusion management


Autoria(s): Gil Pérez, Manuel; Mateos Lanchas, Verónica; Fernández Cambronero, David; Martínez Pérez, Jose Luis; Villagra González, Victor Abraham
Data(s)

2013

Resumo

Security intrusions in large systems is a problem due to its lack of scalability with the current IDS-based approaches. This paper describes the RECLAMO project, where an architecture for an Automated Intrusion Response System (AIRS) is being proposed. This system will infer the most appropriate response for a given attack, taking into account the attack type, context information, and the trust and reputation of the reporting IDSs. RECLAMO is proposing a novel approach: diverting the attack to a specific honeynet that has been dynamically built based on the attack information. Among all components forming the RECLAMO's architecture, this paper is mainly focused on defining a trust and reputation management model, essential to recognize if IDSs are exposing an honest behavior in order to accept their alerts as true. Experimental results confirm that our model helps to encourage or discourage the launch of the automatic reaction process.

Formato

application/pdf

Identificador

http://oa.upm.es/25985/

Idioma(s)

eng

Publicador

E.T.S.I. Telecomunicación (UPM)

Relação

http://oa.upm.es/25985/1/INVE_MEM_2013_161973.pdf

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6603893

info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/CISIS.2013.44

Direitos

http://creativecommons.org/licenses/by-nc-nd/3.0/es/

info:eu-repo/semantics/openAccess

Fonte

Seventh International Conference Complex, Intelligent, and Software Intensive Systems (CISIS) | Seventh International Conference Complex, Intelligent, and Software Intensive Systems (CISIS) | 03/07/2013 - 05/07/2013 | Taichung, Taiwan

Palavras-Chave #Telecomunicaciones #Informática
Tipo

info:eu-repo/semantics/conferenceObject

Ponencia en Congreso o Jornada

PeerReviewed