Ranking a given set of items is a fundamental problem with widespread applications in information retrieval, recommendation systems, and beyond. Crowdsensing data trading (CDT) systems provide an effective means to collect opinions from distributed workers for ranking tasks. However, these systems face several challenges, including preserving privacy, maintaining ranking accuracy, optimizing worker participation, and ensuring incentive compatibility for all stakeholders. To address these challenges, we propose OPAR-IC, an integrated framework designed to enhance efficiency and privacy in CDT systems. The proposed framework integrates a multi-armed bandit (MAB) based approach to dynamically adjust ranking granularity, a hybrid privacy-preserving mechanism combining logarithmic and binary methods to safeguard worker data, and a combinatorial MAB model for efficient worker recruitment. To enhance worker participation and ensure incentive compatibility among all stakeholders, we integrate a hierarchical Stackelberg game into the framework, balancing competing incentives and achieving equilibrium. Our approach is thoroughly validated through theoretical analysis, offering theoretical guarantees for privacy, ranking accuracy, worker participation, and incentive compatibility. Extensive experiments on real-world datasets demonstrate significant improvements in adaptive ranking accuracy, privacy preservation, worker recruitment, and three-party incentive compatibility.

Graph neural networks (GNNs) have achieved impressive results in various graph learning tasks. Backdoor attacks pose a significant threat to GNNs, with a focus on dirty-label attacks. However, these attacks often necessitate the inclusion of blatantly incorrect inputs into the training set, rendering them easily detectable through simple filtering. In response to this challenge, we introduce Clean-Label Graph Backdoor Attack (CGBA). The majority of features in the generated poisoned nodes align with their true labels, significantly enhancing the difficulty of detecting the attack. Firstly, leveraging the uncertainty inherent in the GNNs, we develop a low-budget strategy for selecting poisoned nodes. This approach focuses on nodes in the target class with uncertain and low-degree classifications, allowing for efficient attacks within a limited budget while mitigating the impact on other clean nodes. Secondly, we present an innovative strategy for generating feature triggers. By boosting the confidence of poisoned samples in the target class, this tactic establishes a robust association between the trigger and the target class, even without modifying the labels of poisoned nodes. Additionally, we incorporate two constraints to reduce disruption to the graph structure. In conclusion, comprehensive experimental results unequivocally showcase CGBA's exceptional attack performance across three benchmark datasets and four GNNs models. Notably, the attack targeting the GraphSAGE model attains a 100% success rate, accompanied by a marginal benign accuracy drop of no more than 0.5%.

Federated learning has emerged as a popular paradigm for distributed machine learning, enabling participants to collaborate on model training while preserving local data privacy. However, a key challenge in deploying federated learning in real-world applications arises from the substantial heterogeneity in local data distributions across participants. These differences can have negative consequences, such as degraded performance of aggregated models. To address this issue, we propose a novel approach that advocates decomposing the skewed original task into a series of relatively balanced subtasks. Decomposing the task allows us to derive unbiased features extractors for the subtasks, which are then utilized to solve the original task. Based on this concept, we have developed the FedBS algorithm. Through comparative experiments on various datasets, we have demonstrated that FedBS outperforms traditional federated learning algorithms such as FedAvg and FedProx in terms of accuracy, convergence speed, and robustness. The main reason behind these improvements is that FedBS addresses the data heterogeneity problem in federated learning by decomposing the original task into smaller, more balanced subtasks, thereby more effectively mitigating imbalances during model training.

Large language models (LLMs), with their powerful natural language processing capabilities, can provide more advanced intelligent services for edge devices. However, deploying LLMs at the edge is vulnerable to jailbreak attacks, which can cause the model to generate unsafe content. Meanwhile, current jailbreak attack schemes are inefficient in generating highly stealthy jailbreak prompts. To address this, we propose a Fast and Controllable Bias-Guided Jailbreak Attack (FCB) scheme. First, to improve attack efficiency, we optimize the bias of the model’s output layer to guide the model in generating low-energy jailbreak prompts by directly adjusting the output layer’s logits, thereby accelerating the decoding process. Second, to enhance the stealthiness of the generated jailbreak prompts, we design token stop selection and bias normalization methods to constrain the perturbations during the iterative process, preventing the generation of jailbreak prompts without meaningful semantics. Finally, extensive experimental results demonstrate that FCB can generate highly stealthy jailbreak prompts within a short time. Specifically, compared to the current state-of-the-art controllable attack generation scheme, COLD Attack, FCB achieves up to a 8% improvement in attack success rate, reduces perplexity by up to 181.171, and shortens generation time by as much as 28 seconds.

The symbiotic Internet of Things (IoT) computing paradigm leverages high-speed transmission technologies, such as 6G, to execute large-scale model computations while preserving data privacy, thereby mitigating significant economic losses from data breaches. This paradigm has emerged as a pivotal focus in industrial intelligent manufacturing research. However, within this framework, edge devices must concurrently support both large-scale model computations and high-precision industrial core tasks, creating critical challenges in system efficiency and stability that impede technological advancement. To address these issues, this article introduces a novel Quality of Experience (QoE)-driven heuristic task resource scheduling algorithm that employs Composite Differential Evolution (CoDE) integrated with a tabular Kolmogorov–Arnold Network (KANTab), specifically designed for the comprehensive processes of industrial intelligent manufacturing systems. This methodology enables precise simulation of extensive industrial task requirements and efficient allocation of computational resources under constrained conditions, effectively resolving the resource allocation conflict between large-scale model computation tasks at the edge and primary tasks on terminal devices. We evaluate our approach on a custom-built large-scale task demand dataset from refrigerator manufacturing and demonstrate that it achieves superior overall performance compared to state-of-the-art algorithms.

As the Industrial Internet of Things (IIoT) expands, the number of stakeholders increases. Many entities require significant amounts of data from industrial devices. These data streams improve information sharing and optimize the industrial chain. However, IIoT’s enormous data volumes pose challenges to traditional encryption methods, which are unable to meet efficiency and energy consumption requirements. Thus, a new, efficient encryption algorithm is essential for managing data flows among IIoT subscribers. In this paper, we propose a fast, low-energy, high-security encryption scheme to manage multi-entity data streams. First, we introduce a novel encryption scheme based on the Subset Sum Problem (SSP), which improves energy efficiency and speed. Second, to meet subscription requirements, we employ attribute-based encryption (ABE) for key forwarding. Finally, we handle subscription revocations with device key updates. The device owner utilizes their secret value to generate an identity proof with a key update request.

Using hyperscale surveillance cameras, seamless target tracking can be accomplished in urban security scenarios, significantly enhancing public security and emergency response capabilities. In spite of the advantage of edge computing, tracking multiple targets using multiple cameras would incur prohibitive high computation costs. Based on the deployment of real-world cameras, this research finds that existing tracking scheduling is inefficient as a result of redundant and excessive activation of cameras. As a follow-up, the research proposes a hierarchical tracking framework called CityTrac that leverages fine-grained target movement predictions to provide efficient tracking in hyperscale cameras. First, CityTrac uses a specially designed camera selection strategy that ensures accurate tracking with a minimum number of cameras. After that, CityTrac constructs a probabilistic target movement graph by using historical tempo-spatial correlation information. Using the graph as a model, the tracking scheduling and camera selection problem are formulated as an optimization problem with efficiency-accuracy tradeoff constraints. The research addresses this NP-hard problem using greedy optimization. The experiments conducted with the Cityflow and Geolife datasets demonstrate that, compared with two baselines, CityTrac requires significantly fewer computation resources (over 90%) in order to track the same number of targets with the same level of accuracy.

The proliferation of Internet of Things (IoT) devices has led to a surge in data generation within smart home environments. This data explosion has raised significant privacy concerns and highlighted a lack of user-friendly controls. Consequently, there is a pressing need for a robust privacy-enhancing mechanism tailored for smart homes, safeguarding sensitive data from a user-centric perspective. In this paper, we introduce the Fog-enhanced Personalized Differential Privacy (FEPDP) model, which utilizes the distributed nature of fog computing to improve data processing efficiency and security in smart homes. Specifically, the personalization, as a key feature of FEPDP, is manifested through an array of user-driven policy specifications, enabling home users to specify secret and privacy specifications for their personal data. These specifications not only enhance control over personal data but also align with the heterogeneous nature of smart home environments. Subsequently, aligned with fog-based smart home architecture, we propose two policy-driven partitioning mechanisms that utilize threshold partitioning based on dynamic programming to effectively implement FEPDP. Finally, comprehensive theoretical analysis and experimental validation across various statistical analysis tasks and datasets confirm that FEPDP achieves a superior privacy-utility trade-off for smart home data by leveraging non-sensitive data and fog-based partitioning.

With the development of communication networks, user demand for privacy protection has been growing, and the distributed data structure also brings corresponding challenges. As awareness of the ‘the right to be forgotten’ in privacy protection increases, federated unlearning has become an important solution. Current research mainly focuses on the unlearning of specific classes, but traditional training is typically based on the order of instances. Consequently, precise and efficient unlearning of random samples is required. To address this issue, we present a novel GA-AL scheme that can quickly and effectively achieve the unlearning of random samples. Firstly, when a unlearning request is made, the gradients of all target samples are calculated. The model is then updated in reverse, and the angle of the gradient ascent is adaptively adjusted based on the current gradient direction. Subsequently, lifelong learning is incorporated to make the model more adaptable to the remaining samples, minimizing performance loss. Finally, experiments are conducted on three different types of datasets and models, focusing on four aspects: unlearning effect, efficiency, remaining data performance, and privacy protection. Compared to the Retrain baseline, the GAAL scheme has a performance gap of less than 10% and achieve a 130 times acceleration. This performed best of all experimental methods.

Federated learning (FL) enables multiple data owners to collaboratively train a global model without sharing their private training data. However, FL is threatened by the model poisoning attacks. Most existing defense methods rely on a clean validation dataset on the server to identify attacks, which contradicts the privacy-preserving principle of FL. Furthermore, defending against a substantial proportion of malicious clients remains a significant challenge. This paper proposes CT-BRFL, a Byzantine-robust FL framework where clients guide trust. Experiments show that even one benign client can effectively guide trust. The server starts with an initial trusted set of benign clients and iteratively selects new trusted clients based on guidance from existing ones. At the same time, it detects and removes potential malicious clients to maintain reliability. Trusted clients update the global model using a robust aggregation rule. Extensive experiments are conducted with five aggregation rules under six model poisoning attacks on two datasets. The experimental results demonstrate that on MNIST, CT-BRFL can achieve a detection accuracy of 100% even with more than 50% Byzantine clients, while improving model accuracy by up to 25%.

Currently, important privacy data of the Internet of Things (IoT) face extremely high risks of leakage. Attackers persistently engage in continuous attacks on terminal devices to obtain private data of crucial importance. Although significant progress has been made in recent years in deep reinforcement learning defense strategies, most defense methods still face problems such as low defense resource allocation efficiency and insufficient defense coordination capabilities. To solve the above problems, this paper constructs a novel adversarial security scenario and proposes a security game model that integrates defense resource allocation and patrol inspection. Regarding the above game model, this paper designs a deep reinforcement learning algorithm named SDSA to calculate its security defense strategy. SDSA calculates the allocation strategy of the best patrolling strategy that is most suitable for the defender by searching the policy on a multi-dimensional discrete action space, and enables multiple defense agents to cooperate efficiently by training a multi-intelligent Dueling Double Deep Q-Network (D3QN) with prioritized experience replay. Finally, the experimental results show that the SDSA-learned security defense strategy can provide a feasible and effective security protection strategy for defenders against attacks compared to the MADDPG and OptGradFP methods.

Dynamic graph neural networks (DGNNs) have demonstrated their extraordinary value in many practical applications. Nevertheless, the vulnerability of DNNs is a serious hidden danger as a small disturbance added to the model can markedly reduce its performance. At the same time, current adversarial attack schemes are implemented on static graphs, and the variability of attack models prevents these schemes from transferring to dynamic graphs. In this paper, we use the diffused attack of node injection to attack the DGNNs, and first propose the node injection attack based on structural fragility against DGNNs, named Structural Fragility-based Dynamic Graph Node Injection Attack (SFIA). SFIA firstly determines the target time based on the period weight. Then, it introduces a structural fragile edge selection strategy to establish the target nodes set and link them with the malicious node using serial inject. Finally, an optimization function is designed to generate adversarial features for malicious nodes. Experiments on datasets from four different fields show that SFIA is significantly superior to many comparative approaches. When the graph is injected with 1% of the original total number of nodes through SFIA, the link prediction Recall and MRR of the target DGNN link decrease by 17.4% and 14.3% respectively, and the accuracy of node classification decreases by 8.7%.

The massive amount of data on the Internet of Things (IoT) drives recommendation systems (RSs) based on graph neural network (GNN) to fully play a role in improving user experience. However, data sharing and centralized storage can pose serious security threats. Even though federated learning (FL) can render data “available but not visible,” the heterogeneity of graph data within IoT institutions can result in limitations in recommendation performance. To address the issues, we propose a privacy-preserving decentralized cross-institutional federated graph learning framework called DCI-PFGL for IoT service recommendation, which alleviates the negative impact of data heterogeneity while protecting data security. Our approach extracts graph feature embeddings using the shortest path graph kernel. These embeddings are then anonymized and compared on a blockchain through smart contracts, which helps match partner IoT institutions with lower data heterogeneity. Subsequently, IoT institutions within the same partition collaborate in federated graph learning. We also ensure the protection of transmitted information through differential privacy measures. Finally, we conduct comprehensive experiments on two benchmark data sets. Results demonstrate that DCI-PFGL outperforms other approaches in terms of system accuracy and collaboration costs.

Contemporary research on adversarial attacks in Intelligent Internet of Things focuses on balancing two key aspects: transferability and imperceptibility. However, achieving a balance between these aspects can be challenging. To address this, we introduce an ensemble adversarial attack method based on model interpretability. This method aims to maintain the transferability of attacks while ensuring a high degree of imperceptibility. Our method generates adversarial perturbations by leveraging information from multiple models, thereby enhancing the transferability of adversarial examples. We also increase the aggressiveness of these examples by accentuating the differences in class activation mappings between adversarial and benign images. During the perturbation optimization process, class activation mappings are utilized to generate more selective perturbations, improving the imperceptibility of the adversarial examples. Experimental results demonstrate that our method effectively balances transferability and imperceptibility. Specifically, for 13 victim classifiers, compared to the most potent attack, VNIFGSM, among nine benchmark methods, OUR demonstrates a 10.31% increase in the mean of Attack Success Rate (mASR) in non-targeted attacks, and OUR’s mASR increases by 9% in targeted attacks. Meanwhile, while OUR exhibits comparable attack performance to VNIFGSM, its imperceptibility demonstrates outstanding performance.

Digital Twins (DTs) can generate digital replicas for mobile networks (MNs) that accurately reflect the state of MN. Machine learning (ML) models trained in DT for MN (DTMN) virtual environments can be more robustly implemented in MN. This can avoid the training difficulties and runtime errors caused by MN instability and multiple failures. However, when using data from various devices in the MN system, DTs must prioritize data privacy. Federated learning (FL) enables the construction of models without data leaving devices to protect DTMN data privacy. Nevertheless, FL’s privacy protection needs further improvement for it only guarantees device-level data ownership but ignores that models may retain private information from data. Therefore, this paper focuses on data forgetting in privacy protection, and proposes a novel FL-based unlearning framework (FedME2), which contains MEval and MErase modules. Guided by memory evaluation information from MEval and employing MErase’s multi-loss training approach, FedME2 gets accurate data forgetting in DTMN. In four DTMN virtual environments, FedME2 achieves an average data forgetting rate of approximately 75% for global models under FL and kept the influence on global models’ accuracy below 4%. FedME2 has better data forgetting and improves DTMN data privacy protection while guaranteeing model accuracy.

As one of the key technologies to enable the internet of things (IoT), cloud computing plays a significant role in providing huge computing and storage facilities for large-scale data. Though cloud computing brings great advantages, new issues emerge, such as data security breach and privacy disclosure. In this paper, we introduce a novel secure and privacy-preserving outsourcing computing scheme (hereafter referred to as SPOCS) to tackle this issue. In SPOCS, the effective use of Intel SGX, one of the trusted execution environment (TEE), ensures the confidence and integrity of sensitive data in cloud computing and prevents data loss from causing privacy disclosure. In order to keep malicious cloud service providers (CSPs) from illegally tampering with the outsourcing results, blockchain is employed to ensure the data immutability. Significantly, our proposed scheme achieves anonymity and traceability. In the outsourcing process, smart contracts are applied to make the whole process fully automated without any human involvement. Finally, the security of the proposed scheme is analyzed in terms of its resistance to different attacks. The experiments indicate that our scheme is effective and efficient.

GCN-based clustering schemes cannot interactively fuse feature information of nodes and topological structure information of graphs, leading to insufficient accuracy of clustering results. Moreover, the deep clustering model based on graph structure is vulnerable to the attack of adversarial samples leading to the reduced robustness of the model. To solve the above two problems, this paper proposes a robust clustering model based on attention mechanism and graph convolutional network (GCN), named AG-cluster. This model firstly uses graph attention network and GCN to learn the feature information of nodes and the topological structure information of graphs, respectively. Then the representation results of the above two learning modules are interactively fused by the interlayer transfer operator. Finally, the model is trained end-to-end using a self-supervised training module to optimize the clustering results of the model. In particular, an efficient graph purification defense mechanism (GPDM) is designed to resist adversarial attacks on graph data to improve the robustness of the model. Experimental results show that AG-cluster outperforms the other four benchmark methods, specifically, AG-cluster improves 7.6% in Accuracy and 11.5% in NMI compared to the best benchmark method. Besides, the new model still shows higher robustness and stronger transferability under multiple attacks.

The reduction in the number of queries to the object model is a hot topic in the current research of black-box adversarial attack methods. To solve this problem, in this article, we propose generating adversarial examples with shadow model (GASM) that shifts the number of queries to the object model to the shadow model. The method first determines the shadow model based on the robustness and transferability of classifiers and fine-tunes the decision boundary of the shadow model by constructing adversarial datasets. Second, accesses the shadow model and constructs adversarial examples by maximizing the output probability of the targeted class (any class other than the current one) to modify the image gradient information. Finally, the results show that GASM has the strongest transferability and outperforms white-box attacks when AlexNet (MNIST), VGG-19 (CIFAR10), and MobileNet v2 (Tiny ImageNet) are selected as shadow models.

Currently, the conventional mode of power data transaction is mediated by Web pages. Nevertheless, there are challenging issues such as privacy protection, transaction security and data reliability in power data trading. In this paper, we present a novel secure power data trading scheme (SPDTS). Firstly, the zero-knowledge proof is employed to achieve data availability and consistency without revealing the data. Then, SPDTS takes full advantage of the dispersibility and immutability of blockchain to ensure the reliability of data transactions. To keep the transaction process efficient, the processing tasks for power data are performed under smart contract. Meanwhile, a trusted execution environment (TEE) is adopted to guarantee the security of power data. Finally, we present a differential privacy scheme to safeguard the privacy information in the power data. Our study indicates that the proposed scheme can achieve privacy protection, transaction security and data reliability. Also, we conduct security analysis and verify the privacy protection property of the scheme in real cases.

Internet of Things realizes the leap from traditional industry to intelligent industry. However, it makes edge devices more vulnerable to attackers during processing perceptual data in real time. To solve the above problem, we use the zero‐sum game to build the interactions between attackers and edge devices and propose an antiattack scheme based on deep reinforcement learning. Firstly, we make the k NN‐DTW algorithm to find a sample that is similar to the current sample and use the weighted moving mean method to calculate the mean and the variance of the samples. Secondly, to solve the overestimation problem, we develop an optimal strategy algorithm to find the optimal strategy of the edge devices. Experimental results prove that the new scheme improves the payoff of attacked edge devices and decreases the payoff of attackers, thus forcing the attackers to give up the attack.

The existence of botnet puts people in an extremely insecure environment, which has seriously affected the development of Internet of Things (IoT). In order to prevent the formation of a botnet, it is necessary to understand the propagation behaviors and influence factors. As IoT devices become more intelligent, they can gradually generate social characteristics by mimicking human behaviors. However, in the process of coping with the botnet problem, little consideration has been given to the potential social characteristics of IoT. In this article, we build a dynamic botnet propagation model (i.e., IoT-BSI model) to study the influences of two social characteristics (i.e., device's spread capability and device's identification ability) on botnet formation. First, in terms of device's spread capability, this article makes great improvements on the K- shell decomposition algorithm and calculates this characteristic more accurately. Second, this article divides the device's identification ability into the rational identification ability and the irrational identification ability based on the sociological theory, and the calculation of the former is mainly realized by utilizing the PageRank idea. Third, this article applies the mean-field equation theory to analyze the dynamic characteristics of botnet propagation theoretically. Finally, the comprehensive results show that our model is not merely more consistent with the actual situation but also performs better than four compared models.

The construction of smart cities is the concentrated embodiment of the application of Internet of Things (IoT), which provides a variety of solutions to various problems faced by urban development and urban management. However, the openness, dynamics, and heterogeneity of city IoT increase the risk of attacks, especially the social attributes contained in such systems accelerate virus propagation. To reduce the risk and harm of virus propagation, it is of great significance to analyze the mode and the characteristics of virus propagation. This article proposes a dynamic virus propagation model [i.e., Ignorant-DKs- HN-Exposed-Pvirus-Spread-Recover (IDEPSR)], which focuses on two social attributes (i.e., intelligent device's propagation capability and identification ability). First, this article presents a new algorithm (i.e., DKs-HN) to measure the first attribute based on the evidence theory and an improved K-shell method. The DKs- HN merges the direct influence of a particular node and the indirect influence of 1-hop neighbors by utilizing the combination rules of the evidence theory. Second, this article proposes a Pvirus method to measure the second attribute based on the social hierarchy theory and the nonnegative matrix factor method. Every intelligent device can identify its trust relationship with other devices by using the Pvirus, and then the probability of virus activation lurking in devices can be predicted. Finally, a real data set is used to simulate a social-aware city IoT environment. Convincing experimental results show that the IDEPSR is more reasonable in design and good in performance. The IDEPSR performs better in controlling the virus propagation than the other five models.

Crowdsourcing uses collective intelligence to finish complicated tasks and is widely applied in many fields. However, the crowdsourcing dilemmas between the task requester and the task completer restrict the efficiency of system severely, e.g., the cooperation dilemma leads to the failure in the interactions and the quality of service dilemma results in the inability of task completer to provide high-quality service. Current research usually focuses on solving only one aforementioned dilemma and fails to integrate perfectly with the service architectural pattern of crowdsourcing systems. In this article, combined with the crowdsourcing interaction phase, we limit the objects that cause dilemma and propose a$\boldsymbol {t}$wo-stage$\boldsymbol {g}$ame$\boldsymbol {p}$ayoff$\boldsymbol {d}$ecision-making scheme (TGPD) to overcome these shortcomings. To solve the cooperation dilemma between the requester and the crowdsourcing platform, we first propose a dynamic payment method based on the reputation-quality rules for the task requester, and then develop a cos-evaluation algorithm to estimate platform’s cost, last design a co-determine algorithm to determine whether the platform adopts a cooperative strategy. To address the quality of service dilemma between the crowdsourcing platform and the workers, we first present an auction-screening method to estimate the reasonable recruitment range of workers which can be optimized by the result of cos-evaluation algorithm, and then use a reward distribution method to motivate workers to complete tasks with high quality and on time. The experimental results indicate that our new scheme successfully increases the worker’s and platforms’ payoffs at the same time, improves the accuracy of screening workers, enhances the worker’s quality of service, and decreases the platform’s cost.

There is an inherent reliance on collaboration among the participants of mobile ad hoc networks in order to achieve the fixed functionalities. However, they are susceptible to the destruction of the malicious attacks or denial of cooperation. Therefore, it becomes obvious that the security issue is urgently needed to be addressed. Over the last few years, many trust-considered countermeasures have been proposed. The design of trust quantification methods is the key of these countermeasures. In this study, we abstract a novel light-weight subjective trust inference framework, which is divided into trust assessment and trust prediction. The process of node trust assessment is based on node's historical behaviours. Then utilizing the obtained trust data sequence, we introduce the SCGM(1,1)-weighted Markov stochastic chain measure to predict node's trust for future decision making. Experimental results have been conducted to evaluate the effectiveness of the proposed trust model. As an important security application, based on the standard On-Demand Multicast Routing Protocol (ODMRP), we make four major improvements which take the issue of trust into consideration, and propose a novel trust-based routing protocol called the On-Demand Trust-Based Multicast Routing protocol (ODTMRP). And finally, convincing experimental results are presented using three routing evaluation metrics.

The concept of social networking is integrated into Internet of things (IoT) to socialize smart objects by mimicking human behaviors, leading to a new paradigm of Social Internet of Things (SIoT). A crucial problem that needs to be solved is how to establish reliable relationships autonomously among objects, i.e., building trust. This paper focuses on exploring an efficient context-aware trustworthiness inference framework to address this issue. Based on the sociological and psychological principles of trust generation between human beings, the proposed framework divides trust into two types: familiarity trust and similarity trust. The familiarity trust can be calculated by direct trust and recommendation trust, while the similarity trust can be calculated based on external similarity trust and internal similarity trust. We subsequently present concrete methods for the calculation of different trust elements. In particular, we design a kernel-based nonlinear multivariate grey prediction model to predict the direct trust of a specific object, which acts as the core module of the entire framework. Besides, considering the fuzziness and uncertainty in the concept of trust, we introduce the fuzzy logic method to synthesize these trust elements. The experimental results verify the validity of the core module and the resistance to attacks of this framework.

The Intelligent Transportation System (ITS) is an important application area of the Cyber-Physical System (CPS). To further promote effective communication between vehicles, vehicular ad hoc networks (VANETs) have been widely used in the ITS. However, the communication efficiency in VANETs is not only affected by the external environment but also more vulnerable to malicious attacks. In order to address the above-mentioned issues, we propose a novel trust-based multicast routing protocol (TMR) to defend against multiple attacks and improve the routing efficiency. In the proposed trust model, direct trust is calculated based on Bayesian theory and indirect trust is computed according to evaluation credibility and activity. The fuzzy logic theory is used to fuzzify the direct and indirect trust values, and then the total trust value of the node is obtained by defuzzification. With the help of the obtained trust values, malicious vehicle nodes are eliminated in the processes of route establishment and route maintenance, and finally, the network establishes trusted and efficient routes for data delivery. Comprehensive simulation experiments show that our new protocol can effectively improve the transmission rate of data packets at the expense of a slight increase in end-to-end delay and control overhead.

A mobile ad hoc network (MANET) is a self-configuring network of mobile nodes connected by wireless links without fixed infrastructure, which is originally designed for a cooperative environment. However, MANETs are subjected to a variety of attacks by malicious nodes, in particular for attacks on the packet routing. Compared with traditional cryptosystem based security mechanisms, trust-enhanced routing protocol could provide a better quality of service. In this study, we abstract a basic decentralized effective trust inference model based on node's behavior assessment, where each peer assigns a trust value for a set of peers of interest. In this model, we introduce the 'voting' mechanism to access the recommending experience (or ratings), in order to reduce the cost of the algorithm design and the system overhead. Then combined with this trust model, a novel trust-enhanced multicast routing protocol (TeMR) is proposed. This new protocol introduces the group-shared tree strategy, which establishes more efficient multicast routes since it uses 'trust' factor to improve the efficiency and robustness of the forwarding tree. Moreover, it provides a flexible and feasible approach in routing decision making with trust constraint and malicious node detection. Experiments have been conducted to evaluate the effectiveness of the proposed protocol.

The performance of ad hoc networks depends on the cooperative and trust nature of the distributed nodes. To enhance security in ad hoc networks, it is important to evaluate the trustworthiness of other nodes without central authorities. An information‐theoretic framework is presented, to quantitatively measure trust and build a novel trust model (FAPtrust) with multiple trust decision factors. These decision factors are incorporated to reflect trust relationship's complexity and uncertainty in various angles. The weight of these factors is set up using fuzzy analytic hierarchy process theory based on entropy weight method, which makes the model has a better rationality. Moreover, the fuzzy logic rules prediction mechanism is adopted to update a node's trust for future decision‐making. As an application of this model, a novel reactive trust‐based multicast routing protocol is proposed. This new trusted protocol provides a flexible and feasible approach in routing decision‐making, taking into account both the trust constraint and the malicious node detection in multi‐agent systems. Comprehensive experiments have been conducted to evaluate the efficiency of trust model and multicast trust enhancement in the improvement of network interaction quality, trust dynamic adaptability, malicious node identification, attack resistance and enhancements of system's security.

The wireless ad hoc networks has been attracting increasing attention of researchers owing to its good performance and special applications. Multicast routing with QoS multi-constraints problem in this network has drawn wide spread attention from researchers who have been using different methods to solve it. One solution to this problem is to merge paths in accordance with a swarm intelligence algorithm after finding paths from source node to destination nodes in order to obtain a multicast tree that satisfies QoS multi-constraints. However, the security of routing paths is not guaranteed. To overcome this shortcoming, in this paper, we introduce the concept of trust into multicast routing problem which is made as another QoS constraint, and finally we propose a multicast trusted routing algorithm with QoS multi-constraints based on a modified ant colony algorithm. In this new algorithm, ants move constantly on the network to find an optimal constrained multicast security tree. Simulation results indicate that our algorithm can quickly find the feasible solution to solve constrained multicast security routing issues. Compared to CSTMAN, the packet delivery ratio of new algorithm is improved.

Due to the nature of distribution and self-organization, Mobile ad hoc networks rely on cooperation between nodes to transfer information. Therefore, one of the key factors to ensure high communication quality is an efficient assessment scheme for risks and trust of choosing next cooperative potential nodes. Trust model, an abstract psychological cognitive process, is one of the most complex concepts in social relationships, involving factors such as assumptions, expectations and behaviors. All above makes it very difficult to quantify and forecast trust accurately. In this paper, based on the theories of fuzzy recognition, we present a pattern of multi-dimensional fuzzy decision making with feedback. The analysis and experimental computation shows that this scheme is efficient in risk assessment of Ad hoc networks.