RobustTrust – A Pro-Privacy Robust Distributed Trust Management Mechanism for Internet of Things

Abstract

In the promising time of the Internet, connected things have the ability to communicate and share information. The Internet of Things (IoT) cannot be implemented unless the security-related concerns have been resolved. Sharing information among different devices can compromise the private information of users. Thus, a suitable mechanism is needed to exclude the risk of malicious and compromised nodes. As follows, trust has been proposed in the literature as a useful technology to maintain users' security. Prior studies have proposed diverse trust management mechanisms to achieve adequate trust. The approach of cross-domain trust management is neglected that requires enormous considerations to address the difficulties related to cross-domain communication. In this paper, a cross-domain robust distributed trust management (RobustTrust) system is proposed, which makes a device fit for assessing trust towards different devices locally. In this system, the trust is divided into three components of security that help IoT nodes to become robust against compromised and malicious devices/nodes. The novelty of the proposed mechanism can be summarized in these aspects: A highly scalable trust mechanism, multiple components of evaluation to enhance robustness against attacks, and use of recommendations along with the feedback to build knowledge. Furthermore, the proposed mechanism is event-driven that helps nodes to evaluate trust more effectively as well as enhance the system efficiency. The proposed work is compared with the available trust evaluation schemes by concentrating on various attributes, such as trustworthiness, usability, and accuracy among others. The RobustTrust is validated by the extensive simulations considering absolute trust value's performance, the accuracy of trust estimation, and several potential attacks.

Conjoint belief adaption functionality for statistically secure communication paradigm for CR‐IIoTs

“…This section briefly surveys state‐of‐the‐art focusing on countering Spectrum Sensing Data Falsification (SSDF) attacks in network environment. The Trust‐reputation 3‐8 and game‐theory 9‐11 ‐based security frameworks have intrigued great attentions in research key groups. In previous studies, 3,11,12 the authors explore statistical interference in licensed spectrum and then adopt a cooperative game theory approach for allocating spectrum holes to in CR‐environment.…”

“…In previous studies, 4,9,13 the authors consider the reputation as a characteristic of probabilities and compute the trust as the statistical expectation of reputation. In other studies, 6,15 the authors only acknowledge self‐observations for trust computation, whereas the authors inother works 12,16 use this trust value for guiding the interaction of legitimate node in such a manner that the internal confronts and collusion attacks are countered. Nevertheless, the authors in previous works 3,17 propose a novel approach for immunizing the trust of a legitimate SU by introducing an analyzing module based on the Link Quality Indicators.…”

“…The authors in revious studies 9,18 propose two interesting game‐theory‐based statistical models: first, for enhancing the secrecy rate in spectrum leasing to selfish, and second, for mitigating the SSDF attacks in a cooperative adversarial communication environment. In previous works, 6,15 the authors propose a distributed trust‐based model for an efficient channel's leasing policy in which a Primary User (PU) willingly leases a portion of its channel access to unlicensed Secondary Users (SUs), and in return, these provide cooperative services by adding intentional jamming to counter eavesdropping and employ cooperative relaying to enhance the quality of communication. Nevertheless, the authors in previous studies 10,12,16,19,20 model the cooperative communication in a spectrum aware environment as a statistical framework and hence propose to employ the cooperative adversarial model for analyzing SSDF's consequences on DSA‐enabled CR‐IIoTs.…”

“…The motivation for CBAF comes from the vital need of a comprehensive security framework for safeguarding the critical infrastructure of CR‐IIoTs deployed in hostile environments for continuous monitoring of assembly lines in the manufacturing industries. State‐of‐the‐art works suggest some basic protocols 1‐9 for countering internal and external threats, but when analyzed meticulously, they lack vital security attributes and are susceptible to adverse effects of substandard channel links. CBAF facilitates a cryptographically secure communication paradigm based on CL‐PKC 1,2 and expedites the computation of absolute belief levels of cognitively active SUs for a statistically stable CSS and an efficient idle spectrum access management.…”

AgriTrust—A Trust Management Approach for Smart Agriculture in Cloud-based Internet of Agriculture Things

“…Without a robust solution, it is not possible to take a complete advantage of the smart agriculture. The major security challenges of IoAT can be summarized as the identification of malicious and compromised nodes [47,48], authenticity [49], confidentiality [50,51], and privacy preservation of users [52]. In IoT, sensors are considered as edge nodes, which do not have enough capability to maintain resilience towards several potential attacks in distinct scenarios, such as smart homes [53], smart universities, and smart agriculture [54].…”