The rapid proliferation of IoT devices has popularized crowdsensing for distributed data collection, where Truth Discovery plays a critical role in inferring reliable information from heterogeneous observations. However, existing privacy-preserving truth discovery schemes face challenges including inefficient verifiability, limited weighting strategies, insufficient robustness, and excessive overhead. In this paper, we introduce VRPMTD, a verifiable, robust, and privacy-preserving multi-dimensional truth discovery framework. We achieve scalable verifiability via CRT based packing of multidimensional measurements together with commitments and a linear homomorphic hash. This design allows the data requester to batch verify aggregated results. To improve accuracy, we design a novel weighting mechanism using a Gaussian radial basis function residual and a sliding-window temporal loss, allowing workers’ weights to reflect both long-term reliability and recent behavior. Additionally, the framework improves robustness under realistic sensing and network failures. To optimize efficiency, we implement a lightweight dual-layer encryption mechanism and a difference-based uploading strategy. Formal security analysis indicates that VRPMTD preserves the input-level confidentiality of workers’ raw measurements and ensures verifiability of outsourced aggregation. Extensive experiments on real-world datasets and IoT devices demonstrate that VRPMTD achieves higher accuracy while incurring lower overhead.

Federated learning (FL), as a distributed machine learning paradigm, enables multiple users to train machine learning models locally using individual data and then update global model in a privacy-preserving aggregated manner. However, in FL, the users model parameters are at risk of a privacy breach. Furthermore, the aggregation server may forge aggregated results. To address these problems, in this paper, we propose SVCA, a secure and verifiable chained aggregation for privacy-preserving federated learning (PPFL) scheme. Specifically, we first group users and construct a chained aggregation structure, then employ secret sharing to prevent the entire group of users dropout, and finally propose a scheme for secure verification of the aggregation result to ensure the result correctness and the security of the verification process. The security analysis shows that SVCA not only protects the privacy of users but also ensures the training integrity. Extensive experimental results demonstrate the practical performance of SVCA without compromising classification accuracy.

In recent years, the Internet of Things (IoT) has penetrated all aspects of our lives through smart cities, health, industries and others that are related to people's livelihood. With the increasing number of IoT devices, more and more personal information is exposed in the network space, which inevitably brings some network security problems. Due to the diversity and heterogeneity of IoT devices, identification of such devices in the complex IoT environments remains a major challenge. Existing deep learning-based device identification methods achieve identification of IoT devices by automatically extracting device traffic features, but usually only single modal features of device traffic are considered, which cannot achieve all-around characterization features of communication traffic and affect the identification results. Therefore, we propose an identification method, termed DMRMTT, that employs a Deep convolutional maxout network and MTT model (Multiple Time-series Transformers) to automatically extract the spatial and temporal features of IoT communication session fingerprints and perform further fusion using the structure of the residual, which makes up for the limitations of the existing methods for studying device traffic. This method can improve the characterization of device traffic behaviour and achieve a more accurate identification of IoT devices. Its efficacy is experimentally validated by using two publicly availbale datasets and compared with existing methods. Results show that our method outperforms other methods in widely used performance metrics and achieves 99.82% identification accuracy, demonstrating its superiority and usefulness in IoT device identification.

Mobile edge computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above-mentioned problems, in this article, the task offloading problem is formulated as an optimization problem, and particle swarm optimization (PSO) and quantum PSO based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared with recent advanced strategies, namely ant colony optimization, multiagent deep deterministic policy gradients, deep meta reinforcement learning-based offloading, iterative proximal algorithm, and parallel random forest.

As Vehicular Ad-hoc Networks (VANETs) bring fantastic revolution to intelligent transportation systems, their own security has become an important research topic. However, authentication security, as the key issue of VANETs’ security, is still facing great challenges. Therefore, this survey first starts with the background of VANETs and then introduces the main security concerns. To distinguish from existing surveys, this paper proposes the security challenges and security properties of VANETs from the perspective of builders and attackers, respectively. Then, we present the necessary and important characteristics of a VANET’s security system including the authenticity of nodes and information, the availability of network systems, the integrity and confidentiality of information, and the non-repudiation of information after transmission. Specifically, attack methods and detection schemes for these characteristics are highlighted in detail and analyzed in terms of their advantages and limitations, which fill the gaps in the existing survey. More importantly, we focus on the authentication schemes proposed in recent years, reporting the latest advances in VANETs. These schemes are analyzed and compared in depth in terms of the security characteristics and attack resistance of authentication, as well as in terms of overhead and efficiency. Finally, this paper summarizes some lessons and discusses several future research directions.

Context: Current image inpainting methods show great effects in different applications such as image editing, object removal, art creation and soon, but lack of editability of the inpainting results and convincing unsupervised features.Objective: To improve the existing methods, an optimized framework for image inpainting purpose is proposed based on hierarchical variational auto-encoder (VAE) as well as some optimization strategies.Method: Firstly, the VAE is used to extract the distribution of the features of the masked image in different scales, however, it will cause the distribution offset of extracted features which is unfavorable for image inpainting.Therefore, an optimal strategy that sampling the effective feature and invalid feature separately to avoid the offset of feature distribution of the masked image is integrated into the framework.To further improve the formulation of the proposed framework, the same encoder is used to realize the conversion from two domains to the same domain, which is a benefit to enhance the extraction of effective feature regions.In addition, we also introduce the cycle consistency constraints and GAN constraints into the framework to supervise the inpainting process.Result: Experimental results on the available image dataset demonstrate the effectiveness and superiority of the proposed framework.

With the continuous development of information technology, the network as the infrastructure of the information age has become an indispensable and vital aspect of our daily lives. With the popularization of 5G technology, the number of handheld devices has increased significantly. Although it has brought great convenience to our production and life, it has also introduced new security risks, making the network more likely to be infiltrated and attacked. Currently, abnormal network traffic detection technology has become a vital part of network security, effectively protecting the network and computer systems from intrusion and maintaining normal operation. In the network abnormal traffic detection experiment based on simulation, most researchers use public and well-known datasets, and different datasets contain different attack samples. When testing on different datasets, the model needs to be retrained, significantly increasing the consumption of computer resources. The paper proposes a wireless network abnormal traffic detection method based on the deep transfer adversarial environment dueling double deep Q-Network (DTAE-Dueling DDQN). First, use the old NSL-KDD dataset to train AE-Dueling DDQN and save the training model weights. Then, use the idea of fine-tuning, transfer the weight of the AE-Dueling DDQN training is completed to the target model, and fine-tune the target model using the newer AWID dataset in the WiFi environment. The experiment compares the current representative deep learning (DL) and deep reinforcement learning (DRL) methods. Experimental results show that our proposed method saves computer resources significantly and achieves good results in all evaluation indicators.

The rapid development of Internet technology has brought great convenience to our production life, and the ensuing security problems have become increasingly prominent. These problems threaten users’ privacy and pose significant security risks to the normal conduct of many aspects of society, such as politics, economy, culture, and people’s livelihood. The growth of the information transmission rate expands the scope of attacks and provides a more attack environment for intruders. Abnormal detection is an effective security protection technology that can monitor network transmission in real-time, effectively sense external attacks, and provide response decisions for relevant managers. The development of machine learning has also led to the development of abnormal traffic detection technology. The goal has been to use powerful and fast learning algorithms to deal with changing threats and respond in real-time. Most of the current abnormal detection research is based on simulation, using public and well-known datasets. On the one hand, the dataset contains high-dimensional massive data, which traditional machine learning methods cannot be processed. On the other hand, the labeled data scale is far behind the application requirements, and the dataset’s labels are all manually labeled, so the labeling cost is exceptionally high. This paper proposes a semi-supervised Double Deep Q-Network (SSDDQN)-based optimization method for network abnormal traffic detection, mainly based on Double Deep Q-Network (DDQN), a representative of Deep Reinforcement Learning algorithm. In SSDDQN, the current network first adopts the autoencoder to reconstruct the traffic features and then uses a deep neural network as a classifier. The target network first uses the unsupervised learning algorithm K-Means clustering and then uses deep neural network prediction. The experiment uses NSL-KDD and AWID datasets for training and testing and performs a comprehensive comparison with existing machine learning models. The experimental results show that SSDDQN has certain advantages in time complexity and achieved good results in various evaluation metrics.