Biblio

The internet of things (IoT) is consisting of many complementary elements which have their own specificities and capacities. These elements are gaining new application and use cases in our lives. Nevertheless, they open a negative horizon of security and privacy issues which must be treated delicately before the deployment of any IoT. Recently, different works emerged dealing with the same branch of issues, like the work of Yuwen Chen et al. that is called LightPriAuth. LightPriAuth has several drawbacks and weakness against various popular attacks such as Insider attack and stolen smart card. Our objective in this paper is to propose a novel solution which is “authentication scheme with three factor using ECC and fuzzy extractor” to ensure security and privacy. The obtained results had proven the superiority of our scheme's performances compared to that of LightPriAuth which, additionally, had defeated the weaknesses left by LightPriAuth.

In Internet of Things (IoT) each object is addressable, trackable and accessible on the Internet. To be useful, objects in IoT co-operate and exchange information. IoT networks are open, anonymous, dynamic in nature so, a malicious object may enter into the network and disrupt the network. Trust models have been proposed to identify malicious objects and to improve the reliability of the network. Recommendations in trust computation are the basis of trust models. Due to this, trust models are vulnerable to bad mouthing and collusion attacks. In this paper, we propose a similarity model to mitigate badmouthing and collusion attacks and show that proposed method efficiently removes the impact of malicious recommendations in trust computation.