Smart healthcare refers to the innovative medical model that enhances the efficiency and quality of medical services through the use of modern information technology. While smart healthcare brings convenience to both patients and doctors, it also raises issues of medical data security and privacy protection. In smart healthcare, secure identity authentication not only requires the verification of user identities but also ensures that user identity information is not leaked. At the same time, it requires an efficient authentication process to cope with resource-constrained situations. Existing schemes have addressed the issues of identity authentication and the confidentiality of identity information, but they have significant overhead and are not suitable for resource-constrained situations. Additionally, the schemes themselves are vulnerable to attacks. To address these issues, we propose a multi-party authentication scheme for smart healthcare. First, based on certificateless public key cryptography(CLPKC), a certificateless signature scheme(CLS) is introduced, which eliminates the need for complex bilinear pairings and map-to-point hash function computations, enabling batch authentication for multiple users. Second, the scheme incorporates the user’s contextual environment, ensuring secure user identity authentication only under safe access conditions. Finally, blockchain technology is utilized to record access logs, enabling user traceability. Security analysis proves that the proposed scheme can meet security requirements such as conditional privacy protection, unlinkability, resistance to replay attacks, and resistance to collusion attacks. Compared with existing schemes, this scheme has lower computational overhead and communication costs, making it more suitable for lightweight multi-party authentication in smart healthcare scenarios.

With the development of the Internet of Vehicles (IoV), the rapid growth of smart vehicles has generated vast amounts of data, which are of significant value for training intelligent IoV application models. Traditional methods of intelligent model training require centralized collection of raw data, consuming significant communication resources and posing issues such as privacy leakage. Federated learning offers an approach that allows multiple participants to collaboratively train models while protecting data privacy. However, in practical application scenarios, the data among vehicles often exhibits the characteristics of non-independent and identically distributed (Non-IID), which can lead to significant performance differences of the global model across different vehicles, thus posing a challenge to achieving fair collaboration among vehicles. Existing solutions tend to pursue the overall performance of all users, while neglecting the issue of fairness between individual users, which may result in significant differences in accuracy between different users. To address this issue, this paper proposes a fair federated learning algorithm that combines cross-entropy and marginal loss to assess the influence of the vehicle’s local model against the global model, and dynamically adjust the aggregation weight and achieve fair federated learning accordingly. This method was compared with existing methods on multiple datasets, and the experimental results show that the proposed method is superior to other methods in terms of fairness.

With its advantages are gradually emerging, the Internet of Things (IoT) is profoundly changing the way people work and live. Among all IoT applications, medical IoT is partic-ularly important, in which the user can communicate with smart medical device through the hospital gateway node. However, due to the inherent defects of these smart devices and the openness of wireless networks, medical IoT is vulnerable to kinds of attacks, such as impersonation attack and password guessing attack. Unfortunately, there are few authentication schemes for medical IoT at present, while existing three-party schemes have various weakness and are not suitable for medical IoT. Given the sensitivity of patient data and the deadly consequences of attacks on medical devices, there is an urgent need to develop a suitable authentication scheme in Medical IoT Network with high security. To alleviate the above problems, a robust ECC based three-party authentication and key agreement scheme for medical IoT(RAKI) has been proposed, which is secure with random oracle model and the informal security analysis. Besides, the performance comparisons against existing competing three-party schemes indicate that our scheme is efficient for medical IoT.

Data-driven approaches are envisioned to build future Edge-IoT systems that satisfy IoT devices demands for edge resources. However, significant challenges and technical barriers exist which complicate resource management of such systems. IoT devices can demonstrate a wide range of behaviors in the devices resource demand that are extremely difficult to manage. In addition, the management of resources fairly and efficiently by the edge in such a setting is a challenging task. In this paper, we develop a novel data-driven resource management framework named BEHAVE that intelligently and fairly allocates edge resources to IoT devices with consideration of their behavior of resource demand (BRD). BEHAVE aims to holistically address the management technical barriers by 1) building an efficient scheme for modeling and assessment of the BRD of IoT devices based on their resource requests and resource usage; 2) expanding a new Rational, Fair, and Truthful Resource Allocation (RFTA) model that binds the devices BRD and resource allocation to achieve fair allocation and encourage truthfulness in resource demand; and 3) developing an enhanced deep reinforcement learning (EDRL) scheme to achieve the RFTA goals. The evaluation results demonstrate BEHAVE's capability to analyze the IoT devices BRD and adjust its resource management policy accordingly.

Network intrusion detection plays an important role in the Internet of Things systems for protecting devices from security breaches. Facing challenges of the rapidly increasing amount of diverse network traffic, recent research has employed end-to-end deep learning-based intrusion detectors for automatic feature extraction and high detection accuracy. However, deep learning has been proved vulnerable to adversarial attacks that may cause misclassification by imposing imperceptible perturbation on input samples. Though such vulnerability is widely discussed in the image processing domain, very few studies have investigated its perniciousness against network intrusion detection systems (NIDS) and proposed corresponding defense strategies. In this paper, we try to fill this gap by proposing Def-IDS, an ensemble defense mechanism specially designed for NIDS, against both known and unknown adversarial attacks. It is a two-module training framework that integrates multi-class generative adversarial networks and multi-source adversarial retraining to improve model robustness, while the detection accuracy on unperturbed samples is maintained. We evaluate the mechanism over CSE-CIC-IDS2018 dataset and compare its performance with the other three defense methods. The results demonstrate that Def-IDS is able to detect various adversarial attacks with better precision, recall, F1 score, and accuracy.

The current security method of Internet-of-Vehicles (IoV) systems is rare, which makes it vulnerable to various attacks. The malicious and unauthorized nodes can easily invade the IoV systems to destroy the integrity, availability, and confidentiality of information resources shared among vehicles. Indeed, access control mechanism can remedy this. However, as a static method, it cannot timely response to these attacks. To solve this problem, we propose an intelligent edge-chain-enabled access control framework with vehicle nodes and roadside units (RSUs) in this study. In our scenario, vehicle nodes act as lightweight nodes, whereas RUSs serve as full and edge nodes to provide access control services. Considering the low accuracy of risk prediction due to limited training sets, we leverage a generative adversarial networks (GANs) to convert the risk prediction to a sequence generation. Moreover, aiming at the problems of gradient disappearance and mode collapse existed in the original GANs, we devise a Wasserstein combined GANs (WCGANs). Simulation results demonstrate that WCGAN has higher prediction accuracy than the original GANs. Additionally, it can also improve the accuracy of access control of risk prediction-based access control (RPBAC) model.

The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent.

The emerging Internet of Things (IoT) is facing significant scalability and security challenges. On one hand, IoT devices are “weak” and need external assistance. Edge computing provides a promising direction addressing the deficiency of centralized cloud computing in scaling massive number of devices. On the other hand, IoT devices are also relatively “vulnerable” facing malicious hackers due to resource constraints. The emerging blockchain and smart contracts technologies bring a series of new security features for IoT and edge computing. In this paper, to address the challenges, we design and prototype an edge-IoT framework named “EdgeChain” based on blockchain and smart contracts. The core idea is to integrate a permissioned blockchain and the internal currency or “coin” system to link the edge cloud resource pool with each IoT device' account and resource usage, and hence behavior of the IoT devices. EdgeChain uses a credit-based resource management system to control how much resource IoT devices can obtain from edge servers, based on predefined rules on priority, application types, and past behaviors. Smart contracts are used to enforce the rules and policies to regulate the IoT device behavior in a nondeniable and automated manner. All the IoT activities and transactions are recorded into blockchain for secure data logging and auditing. We implement an EdgeChain prototype and conduct extensive experiments to evaluate the ideas. The results show that while gaining the security benefits of blockchain and smart contracts, the cost of integrating them into EdgeChain is within a reasonable and acceptable range.

This paper aims at defending the data traffic against an attack in Software Defined Networks (SDNs) by leveraging the flexibility to collect traffic information and control traffic routing in SDN. We first model the interaction between an attacker and a defender as a two-player variable sum game model, and analyze the Nash equilibrium of the proposed game model. By further analyzing the Nash equilibrium, we propose some guidelines to control the traffic routing which can help enhance the traffic security under attacking. Based on these guidelines, we design an online algorithm and an offline algorithm to control the traffic routing. By applying our algorithms to the scenario that the attacker intends to destroy a node to break the traffic, our algorithms can greatly reduce the traffic loss when the network is under attacking.

In mobile crowd sensing (MCS), incentive mechanism is one of the most critical issues, and plays an important role on ensuring the quantity of participants and the coverage rate of sensing task. In this paper, we tackle the problem of stimulating enough smartphone users to join in MCS activities with their smartphones. Moreover, the scenario that the selected users drop out of the sensing tasks with random probability during their sensing processes is also taken into account. Therefore, we propose a novel incentive mechanism called IMRAL - an Incentive Mechanism based on Reverse Auction for Location-aware sensing. IMRAL aims to enhance the participants' enthusiasm by maximizing their expected profits. It consists of two parts: winner selection algorithm and payment determination scheme. In the first part, we formulate a winner selection problem by considering the service coverage of mobile users. Due to the NP-hardness of the problem, we introduce a task-centric method to determine the winning bids with polynomial time complexity. The second part is a payment scheme, which determines the payment to winners by a time proportional share rule to ensure the truthful of IMRAL and consider the effects of the randomness of the selected users, and the winners can obtain the maximum utility. Through rigid theoretical analysis, we demonstrate that the proposed mechanism satisfies the properties of computational efficiency, individual rationality, budget feasibility and truthfulness. Simulation results show that, compared with TRAC (truthful auction for location-aware collaborative sensing in mobile crowdsourcing) and IMC-SS (incentive mechanism for crowdsourcing in the single-requester single-bid-model), the IMRAL can achieve better performance in terms of average user utility and tasks coverage ratio.

A new image sparse representation tool-adaptive contourlet transform (ACT) is introduced in this letter. Adaptive directional partitioning schemes in ACT can match the arbitrary orientation distribution of natural image, which brings sparser representation. The proposed ACT is based on pseudopolar Fourier transform that has similar geometrical structure to fan filter. This characteristic helps ACT out of the difficulty of designing traditional directional filter bank. Simulation results demonstrate that ACT can provide more efficient image sparse representation compared to contourlet transform.