Context-Aware Trustworthy Service Evaluation in Social Internet of Things

Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs’ heterogeneity to cause trust problems and manipulate the behavior to delude devices’ reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively.

Section: Trust Value Calculationmentioning

confidence: 99%

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

Alghofaili

Rassam

2022

“…Khani et al [11] proposed a contextual SIoT trust model including independent and dependent metrics. Independent metrics include advertised and expected QoS while dependent metrics include social similarity friendship, social similarity relations, social similarity community, and contextual feedback of trust.…”

Section: Internet-of-things Trust Modelsmentioning

confidence: 99%

“…Studies such as [93] apply a system-of-systems engineering process to obtain integrated architectures of information fusion systems. Distributed trust management systems have also been explored by many researchers [11,88,94]. A future direction is to apply a system-of-systems engineering process to obtain integrated architectures of trust-based information fusion systems.…”

Section: Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Internet-of-Things and Information Fusion: Trust Perspective Survey

Azzedin

Ghaleb

2019

The advent of Internet-of-Things (IoT) is creating an ecosystem of smart applications and services enabled by a multitude of sensors. The real value of these IoT smart applications comes from analyzing the information provided by these sensors. Information fusion improves information completeness/quality and, hence, enhances estimation about the state of things. Lack of trust and therefore, malicious activities renders the information fusion process and hence, IoT smart applications unreliable. Behavior-related issues associated with the data sources, such as trustworthiness, honesty, and accuracy, must be addressed before fully utilizing these smart applications. In this article, we argue that behavior trust modeling is indispensable to the success of information fusion and, hence, to smart applications. Unfortunately, the area is still in its infancy and needs further research to enhance information fusion. The aim of this article is to raise the awareness and the need of behavior trust modelling and its effect on information fusion. Moreover, this survey describes IoT architectures for modelling trust as well as classification of current IoT trust models. Finally, we discuss future directions towards trustworthy reliable fusion techniques.

“…For example, a digitally signed certificate does not convey if the issuer is an industrial spy, and a digitally signed code does not convey if the code is written by malicious programmers. Behavior issues deal with a wider notion of device’s trustworthiness [ 19 , 20 ] and these issues for IoT devices have just recently started to get attention from the research community [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Building trusted IoT environments is of great importance to achieve the full benefits of IoS.…”

Section: Introductionmentioning

confidence: 99%

Towards Scalable and Efficient Architecture for Modeling Trust in IoT Environments

Ghaleb

Azzedin

2021

The Internet of Services (IoS) is gaining ground where cloud environments are utilized to create, subscribe, publish, and share services. The fast and significant evolution of IoS is affecting various aspects in people’s life and is enabling a wide spectrum of services and applications ranging from smart e-health, smart homes, to smart surveillance. Building trusted IoT environments is of great importance to achieve the full benefits of IoS. In addition, building trusted IoT environments mitigates unrecoverable and unexpected damages in order to create reliable, efficient, stable, and flexible smart IoS-driven systems. Therefore, ensuring trust will provide the confidence and belief that IoT devices and consequently IoS behave as expected. Before hosting trust models, suitable architecture for Fog computing is needed to provide scalability, fast data access, simple and efficient intra-communication, load balancing, decentralization, and availability. In this article, we propose scalable and efficient Chord-based horizontal architecture. We also show how trust modeling can be mapped to our proposed architecture. Extensive performance evaluation experiments have been conducted to evaluate the performance and the feasibility and also to verify the behavior of our proposed architecture.