Intelligent Trust based Security Framework for Internet of Things

Trust models have recently been proposed for Internet of Things (IoT) applications as a significant system of protection against external threats. This approach to IoT risk management is viable, trustworthy, and secure. At present, the trust security mechanism for immersion applications has not been specified for IoT systems. Several unfamiliar participants or machines share their resources through distributed systems to carry out a job or provide a service. One can have access to tools, network routes, connections, power processing, and storage space. This puts users of the IoT at much greater risk of, for example, anonymity, data leakage, and other safety violations. Trust measurement for new nodes has become crucial for unknown peer threats to be mitigated. Trust must be evaluated in the application sense using acceptable metrics based on the functional properties of nodes. The multifaceted confidence parameterization cannot be clarified explicitly by current stable models. In most current models, loss of confidence is inadequately modeled. Esteem ratings are frequently mis-weighted when previous confidence is taken into account, increasing the impact of harmful recommendations.                In this manuscript, a systematic method called Relationship History along with cumulative trust value (Distributed confidence management scheme model) has been proposed to evaluate interactive peers trust worthiness in a specific context. It includes estimating confidence decline, gathering & weighing trust      parameters and calculating the cumulative trust value between nodes. Trust standards can rely on practical contextual resources, determining if a service provider is trustworthy or not and does it deliver effective service? The simulation results suggest that the proposed model outperforms other similar models in terms of security, routing and efficiency and further assesses its performance based on derived utility and trust precision, convergence, and longevity

Distributed ledger technology for trust management optimisation in M2M

Decentralised environments with a high number of end-users and M2M devices used in several M2M services increase the importance of a secure trust management and comprehensive trust evaluation system to avoid frauds from malicious nodes. Blockchain, as part of the distributed ledger technology (DLT), helps to improve the overall security in a decentralised M2M community in various aspects. This research publication summarises several existing trust management and evaluation approaches, concluding with their benefits and limitations. Besides these, it highlights the advantages of the distributed ledger technology with the focus on blockchain and consensus mechanisms. In this context, most relevant consensus mechanisms are reviewed and an optimisation concept is proposed. Finally, a blockchain-based trust evaluation system is presented which can be used for trust evaluation and computation of decentralised M2M services part of an M2M community

Context-induced activity monitoring for on-demand things-of-interest recommendation in an ambient intelligent environment

Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications

Security and blockchain convergence with internet of multimedia things : current trends, research challenges and future directions

The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications. © 2020 Elsevier Lt

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

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 parametrisation of trust. Also, trust decay is inadequately modelled 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 parametrised 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 modelled 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

Trust Modelling and Management for Collaborative and Composite Applications in the Internet of Things

A future Internet of Things (IoT) will feature a service-oriented architecture consisting of lightweight computing platforms offering individual, loosely coupled microservices. Often, an end-user will request a bespoke service that will require a composition of two or more microservices offered by different service providers. This architecture offers several advantages that are key to the realisation of the IoT vision, such as modularity, increased reliability and technology heterogeneity and interoperability. As a result, the adoption of this architecture in the IoT is being extensively researched. However, the underlying complexities of service compositions and the increased security risks inherent in such a massively decentralised and distributed architecture remain key problems. The use of trust management to secure the IoT remains a current and interesting topic; its potential as a basis for service compositions has not been thoroughly researched, however. Security through trust presents a viable solution for threat management in the IoT. Currently, a well-defined trust management framework for collaborative and composite applications on an IoT platform does not exist. In this thesis, a collaborative application refers to the one that enables collaboration among its users to jointly complete certain tasks, whereas a composite application is the one composed of multiple existing services to deliver integrated functionalities. 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 parametrisation of trust. Also, trust decay is inadequately modelled in many current models. In addition, trust recommendations are usually inaccurately weighted with respect to previous trust, thereby increasing the effect of bad recommendations. This thesis focuses on providing solutions to the twin issues of trust-based security and trust-based compositions for the IoT. First, a new model, CTRUST, is proposed to resolve the above stated shortcomings of previous trust models. In CTRUST, trust is accurately parametrised 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 modelled 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. In a second study, the trust properties of service compositions in the IoT, along with the effect of the service architecture on the security and performance of the composed service, are investigated. Novel approaches are considered in relation to trust decomposition and composition, respectively. Relevant trust evaluation functions are derived to guide the compositions, which are used to extend CTRUST into a new trust model, SC-TRUST. SC-TRUST is implemented in a suitable simulation and the results are evaluated. The model reliably guides service compositions while ensuring utility to the end-user. Overall, the analyses and evaluations support the conclusion that the trust models are effective in terms of performance gain and security. The models are scalable and lightweight such that they could be deployed to secure applications and drive meaningful services and collaborations in the envisaged IoT and Web 3.0 sphere