A vision of the future Internet is introduced in such a fashion that various computing devices are connected together to form a network called Internet of Things (IoT). This network will generate massive data that may be leveraged for entertainment, security, and most importantly user trust. Yet, trust is an imperative obstruction that may hinder the IoT growth and even delay the substantial squeeze of a number of applications. In this survey, an extensive analysis of trust management techniques along with their pros and cons is presented in a different context. In comparison with other surveys, the goal is to provide a systematic description of the most relevant trust management techniques to help researchers understand that how various systems fit together to bring preferred functionalities without examining different standards. Besides, the lessons learned are presented, and the views are argued regarding the primary goal trust which is likely to play in the future Internet.

/pdf/trust-management-techniques-for-the-internet-of-things-a-3gs85505mu.pdf

Trust Management Techniques for the Internet of Things: A Survey

Internet of Things (IoT) creates a world where smart objects and services interacting autonomously. Taking into account the dynamic-heterogeneous characteristic of interconnected devices in IoT, demand for a trust model to guarantee security, authentication, authorization, and confidentiality of connected things, regardless of their functionality, is imperative. However, as far as we know, against the centrality of trust-based recommendation mechanisms in the IoT environment, there is no ambient study for investigating its techniques. In this paper, we present a systematic literature review (SLR) of trust based IoT recommendation techniques so far. Detailed classifications based on extracted parameters as well as investigation existing techniques in three different IoT layers put forth. Moreover, the advantages, disadvantages and open issues of each approach are introduced that can expand more frontier in obtaining accurate IoT recommendation in the future.

/pdf/trust-based-recommendation-systems-in-internet-of-things-a-1wtgpm0qim.pdf

Trust-based recommendation systems in Internet of Things: a systematic literature review

Security through trust presents a viable solution for threat management in the Internet of Things (IoT). Currently, a well-defined trust management framework for collaborative applications on the IoT platform does not exist. In order to estimate reliably the trust values of nodes within a system, the trust should be measured by suitable parameters that are based on the nodes’ functional properties in the application context. Existing models do not clearly outline the parametrization of trust. Also, trust decay is inadequately modeled in most current models. In addition, trust recommendations are usually inaccurately weighted with respect to previous trust, thereby increasing the effect of bad recommendations. A new model, CTRUST, is proposed to resolve these shortcomings. In CTRUST, trust is accurately parametrized while recommendations are evaluated through belief functions. The effects of trust decay and maturity on the trust evaluation process were studied. Each trust component is neatly modeled by appropriate mathematical functions. CTRUST was implemented in a collaborative download application and its performance was evaluated based on the utility derived and its trust accuracy, convergence, and resiliency. The results indicate that IoT collaborative applications based on CTRUST gain a significant improvement in performance, in terms of efficiency and security.

/pdf/ctrust-a-dynamic-trust-model-for-collaborative-applications-3y93ji3esb.pdf

CTRUST: A Dynamic Trust Model for Collaborative Applications in the Internet of Things

Towards trustworthy Internet of Things: A survey on Trust Management applications and schemes

Security and Blockchain Convergence with Internet of Multimedia Things: Current Trends, Research Challenges and Future Directions.

In recent years, the Internet of Things (IoT) has been an inseparable part of our lives. IoT is typically heterogeneous in nature and requires interconnection with different types of devices or “things”. Being able to secure such a distributed environment is an onerous task. The heterogeneity of IoT, along with other factors, poses a challenge when it comes to securing communication between these devices. In this paper, we propose a novel IoT trust and reputation model that employs distributed probabilistic neural networks (PNNs) to classify trustworthy nodes from malicious ones. Our model tackles the cold start problem in IoT environments by predicting ratings for newly joined devices based on their characteristics and learns over time. Processing is completely distributed and is handled by the nodes themselves. This guarantees better availability, since there is no single point of failure. Moreover, our model can accommodate the various capabilities and types of IoT devices. Unlike other proposed models in the literature, our model provides different levels of security depending on the sensitivity of the data being transmitted.

An IoT trust and reputation model based on recommender systems

The popularity of the Internet of Things (IoT) and its various applications are growing rapidly. Trust in most IoT networks is presumed implicitly. This implicit assumption of trust can be abused by adversaries to disrupt the network and manipulate reputations of trusted devices. Spoofing devices' identities or forging new identities allows adversaries to masquerade and act as legitimate devices. Devices with forged identities are known as Sybils. In this paper, we propose an IoT trust model that uses permissioned blockchains that utilize Smart Contracts (executable policies) to evaluate trustworthiness of IoT devices by recording and validating IoT devices' identities to prevent Sybil attacks. Additionally, to show effectiveness of our model, we provide a proof-of-concept prototype of our model's key features.

Sarah Asiri

Papers

An IoT trust and reputation model based on recommender systems

A Sybil Resistant IoT Trust Model Using Blockchains