A cross-domain trust model of smart city IoT based on self-certification

Smart city refers to the information system with Internet of things and cloud computing as the core technology and government management and industrial development as the core content, forming a large-scale, heterogeneous and dynamic distributed Internet of things environment between different Internet of things. There is a wide demand for cooperation between equipment and management institutions in the smart city. Therefore, it is necessary to establish a trust mechanism to promote cooperation, and based on this, prevent data disorder caused by the interaction between honest terminals and malicious terminals. However, most of the existing research on trust mechanism is divorced from the Internet of things environment, and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of things devices, resulting in the fact that the research on abstract trust mechanism cannot be directly applied to the Internet of things; On the other hand, various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered. Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals, a cross domain trust model (CDTM) based on self-authentication is proposed. Unlike most trust models, this model uses self-certified trust. The cross-domain process of internet of things (IoT) terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction. At the same time, in order to alleviate the collision attack and improve the accuracy of trust evaluation, the overall trust value is calculated by comprehensively considering the quantity weight, time attenuation weight and similarity weight. Finally, the simulation results show that CDTM has good anti collusion attack ability. The success rate of malicious interaction will not increase significantly. Compared with other models, the resource consumption of our proposed model is significantly reduced

From Personal Experience to Global Reputation for Trust Evaluation in the Social Internet of Things

Trust has been exploring in the era of Internet of Things (IoT) as an extension of the traditional triad of security, privacy and reliability for offering secure, reliable and seamless communications and services. It plays a crucial role in supporting IoT entities to reduce possible risks before making decisions. However, despite a large amount of trust-related research in IoT, a prevailing trust evaluation model has been still debatable and under development. In this article, we clarify the concept of trust in the Social Internet of Things (SIoT) ecosystems and propose a comprehensive trust model called REK that incorporates third-party opinions, experience and direct observation as the three Trust Indicators. As the convergence of the IoT and social network, the SIoT enables any types of entities (physical devices, smart agents and services) to establish their own social networks based on their owners relationships. We leverage this characteristic for inaugurating Experience and Reputation, which are originally two concepts from social networks, as the two paramount indicators for trust. The Experience and Reputation are characterized and modeled using mathematical analysis along with simulation experiments and analytical results. We believe our contributions offer better understandings of trust models and evaluation mechanisms in the SIoT environment, particularly the two Experience and Reputation models. This paper also opens important trust-related research directions in near future

An Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things

5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while Device-to-device (D2D) communication is a promising paradigm for future 5G networks. To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this paper presents an intelligent trust cloud management method. Firstly, an active training mechanism is proposed to construct the standard trust clouds. Secondly, individual trust clouds of the IoMT devices can be established through fuzzy trust inferring and recommending. Thirdly, a trust classification scheme is proposed to determine whether an IoMT device is malicious. Finally, a trust cloud update mechanism is presented to make the proposed trust management method adaptive and intelligent under an open wireless medium. Simulation results demonstrate that the proposed method can effectively address the trust uncertainty issue and improve the detection accuracy of malicious devices

Adaptive and survivable trust management for Internet of Things systems

Abstract The Internet of Things (IoT) is characterized by the seamless integration of heterogeneous devices into information networks to enable collaborative environments, specifically those concerning the collection of data and exchange of information and services. Security and trustworthiness are among the critical requirements for the effective deployment of IoT systems. However, trust management in IoT is extremely challenging due to its open environment, where the quality of information is often unknown because entities may misbehave. A hybrid context‐aware trust and reputation management protocol is presented for fog‐based IoT that addresses adaptivity, survivability, and scalability requirements. Through simulation, the effectiveness of the proposed protocol is demonstrated

Can We Trust Trust Management Systems?

The Internet of Things is enriching our life with an ecosystem of interconnected devices. Object cooperation allows us to develop complex applications in which each node contributes one or more services. Therefore, the information moves from a provider to a requester node in a peer-to-peer network. In that scenario, trust management systems (TMSs) have been developed to prevent the manipulation of data by unauthorized entities and guarantee the detection of malicious behaviour. The community concentrates effort on designing complex trust techniques to increase their effectiveness; however, two strong assumptions have been overlooked. First, nodes could provide the wrong services due to malicious behaviours or malfunctions and insufficient accuracy. Second, the requester nodes usually cannot evaluate the received service perfectly. For this reason, a trust system should distinguish attackers from objects with poor performance and consider service evaluation errors. Simulation results prove that advanced trust algorithms are unnecessary for such scenarios with these deficiencies

Securing Critical IoT Infrastructures with Blockchain-Supported Federated Learning

Network trustworthiness is considered a very crucial element in network security and is developed through positive experiences, guarantees, clarity and responsibility. Trustworthiness becomes even more compelling with the ever-expanding set of Internet of Things (IoT) smart city services and applications. Most of today;s network trustworthy solutions are considered inadequate, notably for critical applications where IoT devices may be exposed and easily compromised. In this article, we propose an adaptive framework that integrates both federated learning and blockchain to achieve both network trustworthiness and security. The solution is capable of dealing with individuals’ trust as a probability and estimates the end-devices’ trust values belonging to different networks subject to achieving security criteria. We evaluate and verify the proposed model through simulation to showcase the effectiveness of the framework in terms of network lifetime, energy consumption, and trust using multiple factors. Results show that the proposed model maintains high accuracy and detection rates with values of ≈0.93 and ≈0.96, respectively

A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications

Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructure, trust management is needed at the IoT and user ends. This paper introduces a Neuro-Fuzzy based Brain-inspired trust management model (TMM) to secure IoT devices and relay nodes, and to ensure data reliability. The proposed TMM utilizes node behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference System and weighted-additive methods respectively to assess the nodes trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2 simulation results confirm the robustness and accuracy of the proposed TMM in identifying malicious nodes in the communication network. With the growing usage of cloud based IoT frameworks in Neuroscience research, integrating the proposed TMM into the existing infrastructure will assure secure and reliable data communication among the E2E devices.Comment: 17 pages, 10 figures, 2 table

A trust model mechanism based on quality of service to reduce energy consumption in the internet of things network

The Internet of Things (IoT) is a network of connected devices that have emerged as a promising technology to handle small network-based devices. In recent years, adoption of this relatively new technology has grown immensely. The energy consumption for IoT devices is regarded as one of the most critical factors affecting IoT networks’ lifespan. Quality of Service (QoS) is considered one of the leading research concerns in IoT networks. Communication between IoT devices needs a suitable and reliable service model to meet the requirements of IoT applications to handle the levels of QoS and maximize network lifespans. Therefore, this study aims to propose a trust model mechanism to provide different levels of QoS (QoST-IoT) and maximize IoT network lifespans. The QoS trust model includes four main steps. The first step is trust level calculation, which is calculated for each of the IoT nodes to find the trust level. Then, in the second step, query trust, the IoT node sends the trust values of various components to the cluster head (CH). The third step involves the clustering of the IoT nodes. Subsequently, the fourth step deals with the trust level update. The experiments conducted in this study revealed that the proposed QoS trust model mechanism (QoST-IoT) reduced the energy consumption compared to the trust model mechanisms previously proposed in the literature. The results of the first simulation round showed that the QoST-IoT outperformed the security & trusted devices in the context of IoT (STD-IoT) by 41.2%, trust-based adaptive security in IoT (TAS-IoT) by 43.7%, and the context-aware and multiservice approach in IoT (Context-IoT) by 45.2%. In addition, the second simulation round showed that the QoST-IoT consumed less energy than STD-IoT by 47.5%, TAS-IoT by 50.5%, and Context-IoT by 53.8%. The findings of this study extend the understanding of designing a QoS trust model with energy consumption reduction for IoT networks, which could be beneficial for researchers, IoT developers, and policymakers

Machine Learning based Trust Computational Model for IoT Services

The Internet of Things has facilitated access to a large volume of sensitive information on each participating object in an ecosystem. This imposes many threats ranging from the risks of data management to the potential discrimination enabled by data analytics over delicate information such as locations, interests, and activities. To address these issues, the concept of trust is introduced as an important role in supporting both humans and services to overcome the perception of uncertainty and risks before making any decisions. However, establishing trust in a cyber world is a challenging task due to the volume of diversified influential factors from cyber-physical-systems. Hence, it is essential to have an intelligent trust computation model that is capable of generating accurate and intuitive trust values for prospective actors. Therefore, in this paper, a quantifiable trust assessment model is proposed. Built on this model, individual trust attributes are then calculated numerically. Moreover, a novel algorithm based on machine learning principles is devised to classify the extracted trust features and combine them to produce a final trust value to be used for decision making. Finally, our model’s effectiveness is verified through a simulation. The results show that our method has advantages over other aggregation methods

TACASHI: Trust-Aware Communication Architecture for Social Internet of Vehicles

[EN] The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects equipped with a powerful multisensor platform, communication technologies, and IP-based connectivity to the Internet, thereby creating a possible social network called Social IoV (SIoV). Ensuring the required trustiness among communicating entities is an important task in such heterogeneous networks, especially for safety-related applications. Thus, in addition to securing intervehicle communication, the driver/passengers honesty factor must also be considered, since they could tamper the system in order to provoke unwanted situations. To bridge the gaps between these two paradigms, we envision to connect SIoV and online social networks (OSNs) for the purpose of estimating the drivers and passengers honesty based on their OSN profiles. Furthermore, we compare the current location of the vehicles with their estimated path based on their historical mobility profile. We combine SIoV, path-based and OSN-based trusts to compute the overall trust for different vehicles and their current users. As a result, we propose a trust-aware communication architecture for social IoV (TACASHI). TACASHI offers a trust-aware social in-vehicle and intervehicle communication architecture for SIoV considering also the drivers honesty factor based on OSN. Extensive simulation results evidence the efficiency of our proposal, ensuring high detection ratios >87% and high accuracy with reduced error ratios, clearly outperforming previous proposals, known as RTM and AD-IoV.Kerrache, CA.; Lagraa, N.; Hussain, R.; Ahmed, SH.; Benslimane, A.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.... (2019). TACASHI: Trust-Aware Communication Architecture for Social Internet of Vehicles. IEEE Internet of Things. 6(4):5870-5877. https://doi.org/10.1109/JIOT.2018.2880332S587058776