A cognitive chronometry strategy associated with a revised cloud model to deal with the dishonest recommendations attacks in wireless sensor networks. (1st December 2018)

• Record Type: Journal Article
• Authors: Khedim, Farah; Labraoui, Nabila; Abba Ari, Ado Adamou
• Is Part Of: Journal of network and computer applications. Volume 123(2018)
• Page Start: 42
• Page End: 56
• Publication Date: 2018-12-01
• Subjects: Trust and reputation systems -- Security -- WSN -- Bad mouthing -- Ballot stuffing -- Cloud model -- ABC; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2018.09.001
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Wireless sensor networks (WSNs) face many security issues. When external attacks can be prevented with traditional cryptographic mechanisms; internal attacks remain difficult to be eliminated. Trust and reputation have been recently suggested by many researches as a powerful tool for guaranteeing an effective security mechanism. They enable the detection and the isolation of both faulty and malicious nodes. Nevertheless, these systems are vulnerable to deliberate false or unfair testimonies especially in the case of dishonest recommendations attacks, i.e. badmouthing, ballot-stuffing and collusion attacks. In this paper, we propose a novel bio inspired trust model for WSNs namely Bee-Trust Scheme (BTS) based on the use of both a modified cloud model and a cognitive chronometry parameter. The objective of the scheme is to achieve both a higher detection rate and a lower false positive rate of dishonest recommendations attacks by allowing the distinction between erroneous recommendations and dishonest ones which has thus far been overlooked by most research work. Simulation results demonstrate that the proposed scheme is both effective and lightweight even when the number of dishonest recommenders is large.