Social Internet Of Things Network Research Articles

Implementation of Trust Model for Social Internet of Things Using Centrality Feed Forward Networks

Recent advances of Internet of Things (IoT) lead to the most promising paradigm called Social Internet of Things (SIoT). These techniques are considered the strong amalgamation of the social networking features with the IoT objects. These networks are characterized by facilitating the IoT objects to establish the social networking between each other. In SIoT, an interconnection of networks formed by considering the important features such a object-object interactions, social relationship, reliable recommendations and mandates the careful attention towards the strong trustworthy connections. Hence Intelligent Trust Model Identification Model System (ITMIS) is required for the SIoT networks to identify the misbehaving objects by selecting only the reliable, credible and trustworthy objects before relying on the services provided by them. However, existing frameworks truly relay on the conventional approaches that is based on linear relationship between the inputs and outputs. These methods may lead to the high misclassification ratio of selecting the untrusted devices that even may cause untrust process in an application. To overcome this problem, in the proposed research work the novel ITMIS which ensembles the non-linear centralities relationships with the architecture Extreme Feed Forward Neural Networks (EFFNN) with the combination of hybrid algorithm of Long Short Term Memory (LSTM) and Gated Recuurent Unit (GRU) for the better accuracy compare with the existing model. The proposed model captures the number of key trust metrics based on centralities measurements and envisages the EFNN to classify the trusty and non-trust objects. The extensive experimentations are conducted using the real world datasets and various trust metrics were evaluated and compared with the other state-of-the-art trust learning models. The results demonstrate that the proposed model has outperformed with the other existing models by maintaining the accuracy of 95% to 94% with the decreasing untrusted rates and It illustrates conclusively that the LSTM's use of enhancing ensemble characteristics has shown to be more advantageous. While other models, like E-LSTM (90%) to 80%, Stacked LSTM (85% to 80%), SVM (85% to 79%), KNN (75% to 68%), and RF (65% to 58%), exhibit decreasing efficiency, the proposed approach illustrates that it is more productive as well as efficient for building an intelligent trust classification system that is appropriate for establishing trusted SIoT network communication.

Implementation of Trust Model for Social Internet of Things Using Centrality Feed Forward Networks

Recent advances of Internet of Things (IoT) lead to the most promising paradigm called Social Internet of Things (SIoT). These techniques are considered the strong amalgamation of the social networking features with the IoT objects. These networks are characterized by facilitating the IoT objects to establish the social networking between each other. In SIoT, an interconnection of networks formed by considering the important features such a object-object interactions, social relationship, reliable recommendations and mandates the careful attention towards the strong trustworthy connections. Hence Intelligent Trust Model Identification Model System (ITMIS) is required for the SIoT networks to identify the misbehaving objects by selecting only the reliable, credible and trustworthy objects before relying on the services provided by them. However, existing frameworks truly relay on the conventional approaches that is based on linear relationship between the inputs and outputs. These methods may lead to the high misclassification ratio of selecting the untrusted devices that even may cause untrust process in an application. To overcome this problem, in the proposed research work the novel ITMIS which ensembles the non-linear centralities relationships with the architecture Extreme Feed Forward Neural Networks (EFFNN) with the combination of hybrid algorithm of Long Short Term Memory (LSTM) and Gated Recuurent Unit (GRU) for the better accuracy compare with the existing model. The proposed model captures the number of key trust metrics based on centralities measurements and envisages the EFNN to classify the trusty and non-trust objects. The extensive experimentations are conducted using the real world datasets and various trust metrics were evaluated and compared with the other state-of-the-art trust learning models. The results demonstrate that the proposed model has outperformed with the other existing models by maintaining the accuracy of 95% to 94% with the decreasing untrusted rates and It illustrates conclusively that the LSTM's use of enhancing ensemble characteristics has shown to be more advantageous. While other models, like E-LSTM (90%) to 80%, Stacked LSTM (85% to 80%), SVM (85% to 79%), KNN (75% to 68%), and RF (65% to 58%), exhibit decreasing efficiency, the proposed approach illustrates that it is more productive as well as efficient for building an intelligent trust classification system that is appropriate for establishing trusted SIoT network communication.

SybilPSIoT: Preventing Sybil attacks in signed social internet of things based on web of trust and smart contract

AbstractSybil attacks are a very serious challenge in social networks including, the Social Internet of Things (SIoT). This paper introduces the SybilPSIoT method, in which a hybrid prevention and detection decentralized approach is proposed in SIoT based on smart contracts. The owner adds his objects to the smart contract. However, hostile owners can create Sybil things. This paper formally presents a model that uses a signed SIoT network with objects and identifiers as network nodes and information about the type of nodes (acknowledgers). Assuming the relationship between the edge marks between nodes and the node type, the proposed method uses trust paths between verification and desired nodes using a Bayesian inference model and structural balance patterns to judge the target node in these paths. It also uses game theory to control access owners to prevent Sybil from creating new things based on a cost‐benefit function. Based on the analysis method, a validating effect proportional to the path length on the target object was presented. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks.

Enhanced trust management for building trustworthy social internet of things network

AbstractInternet of Things (IoT) and wireless communication technologies are evolving rapidly and changing the way we live and work. IoT devices may need to share personal information over the public network taking the help of nearby devices. Thus, the trustworthiness of nearby devices shall play an essential role in providing security and privacy assurance. The authors proposed an autonomous decentralised trust management model for selecting a trustworthy device for a requested service transaction. The proposed scheme uses the Social Internet of Things for trust management. The proposed model uses social relationships to find the level of trust among related devices, estimate the overall trust of the unknown devices, periodically update the observed trust values, and isolate the malicious nodes in the network. The evaluated trust values are updated in the network frequently so that other devices can utilise the information later. The periodic update can increase performance and detect trust‐related malicious attacks. The simulation result shows that the performance of the proposed model is better as compared to existing trust management models. The proposed model can detect malicious objects at an early stage and can handle malicious attacks by isolating malicious devices.

Deep Q-network-based heuristic intrusion detection against edge-based SIoT zero-day attacks

How to process and classify zero-day attacks due to their huge damage to social Internet of Things (SIoT) systems has become a hot research issue. To solve this issue, we propose a heuristic learning intrusion detection system with Deep Q-Networks (DQN) for edge-based SIoT networks under the scenario of insufficient training samples, which is named DQN-HIDS. It is composed of an SIoT network traffic processing module and a DQN-based heuristic learning network. The SIoT network traffic processing module generates SIoT traffic samples, selects samples entering a classifier and a cybersecurity examiner center, and outputs similarity. We integrate DQN into a heuristic learning network to gradually improve its ability to identify malicious traffic. Specially, reward functions are designed according to the selected actions of the network, in order to punish the behavior of incorrectly labeling malicious samples and make variable reward functions adapt to different execution actions. The LSTM-based DQN then maximizes the cumulative expected reward to find the optimal strategy for the heuristic learning network. Consequently, DQN-HIDS gradually improves the behavior frequency of its labeling, reduces resource workloads, and increases the ability to label SIoT network traffic. Experiments show the performance of DQN-HIDS in terms of the workload of the examiner center and the queue workload of delayed samples, the rewards obtained by the DQN-based heuristic learning network, and the accuracy of the classifier. Comparisons with a state-of-the-art deep learning model and typical machine learning methods are also made, demonstrating the advantages of DQN-HIDS with fewer SIoT network traffic samples.

Evolutionary game model based on cumulative prospect theory for information management mechanism in SIoT

As nodes in Social Internet of Things (SIoT) become more intelligent, malicious information occurs more frequently and spreads more widely. This problem can severely affect the trustworthiness of services and applications in SIoT. Methods to effectively control malicious information spreading in SIoT are essential and necessary. Reputation mechanism provides a powerful tool to tackle this challenge. In this paper, we propose a reputation-based mechanism to activate the self-purification capacity of the SIoT network by balancing information conflicts triggered by reporters and supporters. In order to find the best rewarding and punishment strategy, a bilateral cumulative-prospect-based evolutionary game model of SIoT network information conflict is constructed. Using local stability analysis and numerical simulation, the evolutionary trends of the proposed game model under different theoretical application scenarios are analyzed. The findings indicate that the basic income and deposit of both sides, the popularity of information as well as the importance of the conformity effect all have a significant impact on the system's steady state and evolutionary path. The specific conditions that both participating sides of the game tend to treat conflicts relatively rationally are analyzed. Dynamic evolution analysis and sensitivity analysis of selected parameters show that basic income is positively related to smart object's feedback strategies, while deposit is negatively related to that. While weight of conformity effect or the information popularity goes up, the rising of feedback probability is observed. Based on the above results, suggestions on dynamic reward and punishment strategies are given. The proposed model is a helpful attempt to model the evolution of information spreading in SIoT networks, with the ability to simulate several well-known regularities of message dissemination. Proposed model and suggested quantitative strategies can be helpful to build feasible malicious information control facilities in SIoT networks.

WaterPurifier: A scalable system to prevent the DNS water torture attack in 5G-enabled SIoT network

Social Internet of Things (SIoT) network contains a huge number of smart devices and is rich in social behavior relationships between these objects, especially in 5G-abled SIoT network. However, vulnerable devices and assailable applications or services in SIoT give attackers the opportunity to conduct DNS water torture (DNSWT) attacks for domain name system (DNS) infrastructures, which can cause the device to be unavailable, and moreover, previous approaches designed for normal Internet environment cannot reach the high level of demand on scalability and flexibility in SIoT network. In this work, we comprehensively analyze the characteristics of DNSWT attacks in 5G-enabled SIoT network and propose a collaborative and hierarchical defensive system called WaterPurifier to protect the SIoT network from DNSWT attacks. WaterPurifier consists of Gateway Layer, Server Layer and Cloud Layer. An asynchronously communication mechanism is implemented in the defensive system to ensure its flexibility. The components in Gateway Layer and Server Layer execute lightweight functions, like flow forwarding and attack monitoring, to guarantee a high level of scalability. Cloud Layer deploys an end-to-end domain encoding and a real-time training process, which can filter out DNSWT attack traffic effectively and efficiently. We implement a prototype of WaterPurifier in an in-lab environment and do an evaluation on the experiment result with performance indicators like packet loss rate. Both results and evaluations show high effectiveness and efficacy of the proposed system.

Sender anonymity: Applying ring signature in gateway-based blockchain for IoT is not enough

Over the last decade, there has been extensive research into the security and privacy of blockchain technology, from the use of ring signatures to ring confidential transactions. Although existing cryptographic methods perform well on traditional blockchain architectures, they do not provide complete privacy in a hierarchical blockchain architecture for Internet of Things (IoT) devices. The blockchain technology is centered at the gateway layer in the hierarchical blockchain architecture, which can easily reveal the transaction sender using network traffic information, thereby violating the sender’s anonymity. Furthermore, the gateway can perform eclipse attacks against transaction senders on a case-by-case basis. To this end, this paper proposes a novel security and privacy blockchain protocol that is better suited to the hierarchical blockchain architecture and thus more appropriate for the IoT environment. Specifically, it leverages the relationship among IoT devices at the perception layer of the hierarchical blockchain architecture for IoT, and constructs an overlay social IoT network by exploiting devices’ multi-agent capabilities. Furthermore, circuit establishment based on multilayered encryption is applied to conceal the transaction propagation path through the overlay social IoT. Theoretical analysis shows that our novel protocol is flexible, evades a malicious gateway, and is secure under the decisional Diffie-Hellman assumption.

A resilient Trust Management framework towards trust related attacks in the Social Internet of Things

As a huge number of heterogeneous smart things in the Social Internet of Things (SIoT) paradigm are connected to exchange services, security principles are needed to ensure safe communication in the network. From this perspective, various Trust Management models have been set forward in the literature in order to assess nodes trustworthiness and therefore to avoid malicious connections in the SIoT network. However, these models still lack practical solutions to address SIoT trust related attacks performed towards the trust evaluation process. In this regard, the present work stands for an in-depth study on the effectiveness of the trust attack management, as it is integrated into the trust model, with the aim of classifying the nodes behaviors more accurately in order to ensure secure connections in the network. For this reason, nodes behaviors analysis, based on Machine Learning (ML) algorithms, is carried out within our trust assessment process. Our objective lies in limiting interactions with not only poor service provider, but also with attacker nodes. The proposed trust method’s acquired outcome reveals that our introduced TM model demonstrates an improved efficiency in identifying malicious nodes compared to a trust classification algorithm without nodes behaviors evaluation. Experimentation conducted within a simulated dataset based on real social data through Cooja; corroborates the efficiency of our proposed solution in terms of coping with simulated trust related attacks performed by malicious nodes.

Friendship selection and management in social internet of things: A systematic review

The Social Internet of Things (SIoT) paradigm integrates the Internet of Things (IoT) and human social networks. SIoT network plays an essential role in establishing social friendships between smart devices. Any physical object deployed on the SIoT environment will access the required service utilizing its social friendships. Discovering desirable and efficient services among SIoT smart devices is possible only if we create, select, and manage social friendships intelligently and reliably. However, despite the significant importance of the thrust area of social friendship selection and management in the SIoT network, there is no systematic and comprehensive research study about exclusively analyzing and reviewing all the key strategies of this research field. Therefore, this survey paper investigates the state-of-the-art SIoT friendship selection and management strategies based on the Systematic Literature Review (SLR) methodology. It achieves this target by looking at the journal and conference papers published on social friendship selection and Relationship Management (RM) scope between 2013 and December 2020. We propose a novel technical taxonomy to categorize the state-of-the-art studies into the five categories in which social relationship selection and management play a significant role: structure-based, community-based, ontology-based, recommendation-based, and other strategies. In each category, the relevant research studies are presented and described. Also, a summary of the benefits and drawbacks of the reviewed studies is provided, and a comprehensive comparison of studies based on some evaluation metrics is made. Finally, we outline new emerging challenges and discuss and identify future research directions and open issues.

Optimal Placement of Social Digital Twins in Edge IoT Networks.

In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart (twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.

A Query based Information search in an Individual’s Small World of Social Internet of Things

Recently, the consolidation of Social Network and the Internet of Things (IoT) acquiring appreciation and consideration among researchers due to its immense and adaptable behavior. Social Internet of Things (SIoT) comprehends the adequacy to support the inventive applications and network services with adept and persuasive demeanors. The author’s exertion for this research provides the subsistence to the SIoT amelioration Currently, the SIoT technique proven to be competent, but the exponential growth of heterogeneous smart devices may arise a challenging scenario. Searching the right object, service, or best path to the informative nodes in such highly convoluted networks is always a challenging task. This research strengthen the SIoT paradigm by consolidating the properties of the individual’s Small World. In addition, this research also advises a Smart Social Agent (SSA) to minimize the human intervention. The absolute objective of this paper is two-fold, (i) transformation of SIoT network into Individual’s Small World SIoT network, (ii) searching the right information efficiently by exploiting SSA. To accomplish the goal we have proposed an efficient Query based Searching algorithm which exploits Service Oriented properties of Individual’s Small World SIoT network and searches information. First result shows the significant alleviation in the network complexity, reduces the average path length, and network diameter. Eventually, it improves the network scalability and navigability. While second result shows that the proposed Query based searching algorithm proves to be efficient with its mean execution time not more than 2.66 ms for complete network on two different machines. Whereas, for Individual’s small world network the mean execution time is not more than 2.47 ms for both machine.

How to exploit the Social Internet of Things: Query Generation Model and Device Profiles’ Dataset

The future Internet of Things (IoT) will be characterized by an increasing number of object-to-object interactions for the implementation of distributed applications running in smart environments. The Social IoT (SIoT) is one of the possible paradigms that is proposed to make the objects’ interactions easier by facilitating the search of services and the management of objects’ trustworthiness. In this scenario, we address the issue of modeling the queries that are generated by the objects when fulfilling applications’ requests that could be provided by any of the peers in the SIoT. To this, the defined model takes into account the objects’ major features in terms of typology and associated functionalities, and the characteristics of the applications. We have then generated a dataset, by extracting objects’ information and positions from the city of Santander in Spain. We have classified all the available devices according to the FIWARE Data Models, so as to enable the portability of the dataset among different platforms. The dataset and the proposed query generation model are made available to the research community to study the navigability of the SIoT network, with an application also to other IoT networks. Experimental analyses have also been conducted, which give some key insights on the impact of the query model parameters on the average number of hops needed for each search.

Decentralized Self-Enforcing Trust Management System for Social Internet of Things

The Internet of Things (IoT) is the network of connected computing devices that have the ability to transfer valued data between each other via the Internet without requiring human intervention. In such a connected environment, the social IoT (SIoT) has become an emerging trend where multiple IoT devices owned by users support communication within a social circle. Trust management in the SIoT network is imperative as trusting the information from compromised devices could lead to serious compromises within the network. It is important to have a mechanism where the devices and their users evaluate the trustworthiness of other devices and users before trusting the information sent by them. The privacy preservation, decentralization, and self-enforcing management without involving trusted third parties are the fundamental challenges in designing a trust management system for SIoT. To fulfill these challenges, this article presents a novel framework for computing and updating the trustworthiness of participants in the SIoT network in a self-enforcing manner without relying on any trusted third party. The privacy of the participants in the SIoT is protected by using homomorphic encryption in the decentralized setting. To achieve the properties of self-enforcement, the trust score of each device is automatically updated based on its previous trust score and the up-to-date tally of the votes by its peers in the network with zero-knowledge proofs (ZKPs) to enforce that every participant follows the protocol honestly. We evaluate the performance of the proposed scheme and present evaluation benchmarks by prototyping the main functionality of the system. The performance results show that the system has a linear increase in computation and communication overheads with more participants in the network. Furthermore, we prove the correctness, privacy, and security of the proposed system under a malicious adversarial model.

Ranking from Crowdsourced Pairwise Comparisons via Smoothed Riemannian Optimization

Social Internet of Things has recently become a promising paradigm for augmenting the capability of humans and devices connected in the networks to provide services. In social Internet of Things network, crowdsourcing that collects the intelligence of the human crowd has served as a powerful tool for data acquisition and distributed computing. To support critical applications (e.g., a recommendation system and assessing the inequality of urban perception), in this article, we shall focus on the collaborative ranking problems for user preference prediction from crowdsourced pairwise comparisons. Based on the Bradley--Terry--Luce (BTL) model, a maximum likelihood estimation (MLE) is proposed via low-rank approach in order to estimate the underlying weight/score matrix, thereby predicting the ranking list for each user. A novel regularized formulation with the smoothed surrogate of elementwise infinity norm is proposed in order to address the unique challenge of the coupled the non-smooth elementwise infinity norm constraint and non-convex low-rank constraint in the MLE problem. We solve the resulting smoothed rank-constrained optimization problem via developing the Riemannian trust-region algorithm on quotient manifolds of fixed-rank matrices, which enjoys the superlinear convergence rate. The admirable performance and algorithmic advantages of the proposed method over the state-of-the-art algorithms are demonstrated via numerical results. Moreover, the proposed method outperforms state-of-the-art algorithms on large collaborative filtering datasets in both success rate of inferring preference and normalized discounted cumulative gain.

Efficient service search among Social Internet of Things through construction of communities

ABSTRACTSocial Internet of Things is a new paradigm that has integrated two technologies namely Internet of Things (IoT) and Social Networks. IoT is a many vision one paradigm technology whereas Social Networks are platforms where voluminous collaborations between humans exist. The idea of using the collective intelligence gathered by Social Networks in IoT led to the notion of Social Internet of Things (SIoT). SIoT is defined as a social network of objects that are not only smarter but also socially conscious. A fundamental requirement of such a network is efficient service search and discovery mechanisms. This paper proposes a simple algorithm to discover resources/services among SIoT communities. Two key ideas are proposed, namely, i) Detection of communities among established SIoT network and ii) intracommunity and intercommunity service search algorithms for efficient service discovery among SIoT communities. Experimental results prove that the constructed communities are strongly correlated and the efficiency of the proposed algorithms is higher when compared to the existing schemes.

Dynamic trust management for secure communications in social internet of things (SIoT)

The world has faced three Information and Communication Technology (ICT) revolutions and the third ICT wave led to Internet of Things, the notion of anything, everything, anytime and everywhere. Out of the many visions of IoT, one revolutionary concept is to make IoT sociable i.e., incorporating social networking within Internet of Things. This revolution has led to the notion of Social Internet of Things (SIoT). Establishing a SIoT network or community is not so simple and requires integration of heterogeneous technology and communication solutions. This paper focuses on establishing a secure and reliable communication over nodes in SIoT by computing trust dynamically among neighboring nodes. Trust Management is an important area that has attracted numerous researchers over the past few years. The proposed DTrustInfer computes trust based on first hand observation, second hand observation, centrality and dependability factor of a node. Properties of trust such as honesty, cooperativeness, community interest and energy of a node are considered for computing trust. Also, this paper ensures secure communication among SIoT nodes through simple secret codes. Experimental results show that the proposed DTrustInfer outperforms the existing trust models significantly.

Game theory-based Security Vulnerability Quantification for Social Internet of Things

In modern society, information and communication technology (ICT) has been applied to various areas such as home, industry, and finance. Therefore, social networks using Internet of Things (IoT) technologies have been constructed. As ICT continues to be used in various modern applications, security vulnerabilities from legacy ICT have been inherited by social IoT network systems. To guarantee the safety of these applications, these networks must be protected from various cyberattacks. A variety of security technologies and products have been developed for this purpose. However, the most important task in dealing with cyberattacks is to inspect the current security status of a social IoT network system. Many types of vulnerability quantification methods exist for inspecting the security vulnerabilities of network systems. However, with legacy methods, quantification results lack objectivity. In this study, to compensate for this limitation, we propose a game-theory-based vulnerability quantification method using attack tree, which consists of three steps: game strategy modeling, cost-impact analyzation, and payoff calculation. We present a case study for a social-IoT-based network environment. Using the proposed method, we believe social IoT network system security experts will be able to cope with security incidents more effectively. The proposed method can be used as a reference for constructing a safer social IoT network system.

The Social Internet of Things (SIoT) – When social networks meet the Internet of Things: Concept, architecture and network characterization

Recently there has been quite a number of independent research activities that investigated the potentialities of integrating social networking concepts into Internet of Things (IoT) solutions. The resulting paradigm, named Social Internet of Things (SIoT), has the potential to support novel applications and networking services for the IoT in more effective and efficient ways.In this context, the main contributions of this paper are the following: (i) we identify appropriate policies for the establishment and the management of social relationships between objects in such a way that the resulting social network is navigable; (ii) we describe a possible architecture for the IoT that includes the functionalities required to integrate things into a social network; (iii) we analyze the characteristics of the SIoT network structure by means of simulations.