With the development of Sixth Generation (6G) Internet of Things (IoT) technology, ensuring data reliability and security in networks has become a critical issue. To address the identification of abnormal behaviors in network traffic, this study proposes an anomaly traffic detection algorithm combining wavelet analysis and machine learning. By utilizing wavelet analysis, this paper ex-tracts time-frequency features from Fifth Generation (5G) core network traffic data, which effectively capture abrupt changes and periodic fluctuations in the data. Combining deep learning models, particularly dynamic convolution and attention mechanisms, this method adaptively optimizes the feature extraction process, enhancing the model’s sensitivity and accuracy in detecting key traffic features. Experimental results demonstrate that the proposed algorithm outperforms traditional methods in multiple standard datasets, with superior performance in accuracy, precision, recall, and other evaluation metrics.

Data paucity in IoT attack/anomaly traffic samples inhibits accurate threat identification, limiting defensive modeling capabilities. Existing generative models, such as WGAN-GP and CTGAN, often fail to preserve fine-grained temporal dynamics and multi-dimensional feature correlations in packet-level traffic, leading to low fidelity in synthesized data and poor generalization in downstream tasks. To address these limitations, this paper proposes VWRAM, a novel traffic generation framework combining Variational Autoencoder (VAE) with Wasserstein generative adversarial network (WGAN) and residual attention mechanism. It first utilizes Gramian Angular Summation Field (GASF) to transform one-dimensional time-series network traffic data into two-dimensional images, and then performs gamma correction using a power-law expression to enhance image quality. The framework employs a hybrid architecture combining VAE and WGAN to synthesize network traffic feature maps. To enhance the extraction of channel and spatial characteristics, a bidirectional multi-scale channel fusion residual module and a dual attention mechanism (accounting for both channel and spatial dimensions) are incorporated into the generator of GAN. This design effectively bridges the gap in modeling complex, multi-scale IoT traffic patterns with high structural and statistical fidelity.Finally, theoretical analysis and experimental results on the MedBIoT dataset demonstrate that the proposed framework exhibits significant advantages over representative generative models. Specifically, VWRAM achieves an anomaly detection accuracy of 93%, outperforming state-of-the-art models like SyNIG and CTGAN by 6% and 15%, respectively. Furthermore, it demonstrates superior distributional fidelity, achieving the lowest Jensen-Shannon Divergence of 0.0918 in key traffic features.

Leveraging their mobility and feasibility, Unmanned Aerial Vehicles (UAVs) present a promising solution for assisting covert communications to mitigate the risk of eavesdropping. However, existing studies mainly rely on passive optimization, where the UAV adjusts its transmit parameters according to the channel state, without actively balancing covertness constraints and average system throughput. To solve the above challenge, we propose for the first time a UAV relay-assisted covert communication framework with a buffer. Specifically, we derive the optimal detection threshold for the eavesdropper with mobility and uncertain locations, and obtain a closed-form solution for the lowest detection error probability. To solve the formulated average system throughput maximization problem, we transform the covertness constraint into a tractable analytical form, and obtain the optimal transmit power for both the UAV relay and the friendly UAV jammer. Then, through a rigorous theoretical analysis of upper and lower bounds on average system throughput, we prove the existence of optimal UAV trajectories. Finally, optimal transmission and reception decisions of the UAV relay are derived under covertness and buffer size constraints. Numerical results and theoretical analysis demonstrate the effectiveness of the proposed scheme in terms of average system throughput and covert performance.

The proliferation of resource-constrained Internet of Things (IoT) devices poses formidable security challenges, rendering traditional centralized authentication mechanisms impractical. To address this issue, this paper proposes AMAKA (Anonymous Mutual Authentication and Key Agreement), a novel blockchain-fortified protocol specifically designed for IoT environments. AMAKA utilizes smart contracts to establish a robust framework for device lifecycle management, including registration, updates, and revocation, enabling fine-grained access control under the authority of the equipment manufacturer. The protocol's core synergizes Schnorr signatures with noninteractive zero-knowledge proofs to deliver strong guarantees of mutual authentication, user anonymity, unlinkability, perfect forward secrecy, and conditional traceability. We formally verify AMAKA's security against a wide range of attacks by employing the ProVerif tool under an active adversary model. Furthermore, a prototype deployed on a private Ethereum network demonstrates its practical viability, confirming low on-chain overhead, minimal storage demands, and high computational efficiency. Therefore, AMAKA provides a balanced, secure, and scalable authentication solution for large-scale IoT ecosystems.

Federated learning (FL) in Internet of Things (IoT) environments is impeded by model leakage, prohibitive computational overhead, and the lack of fair incentive mechanisms. To overcome these challenges,this paper proposes the PrivacyPreserving and Fair Payment framework based on Blockchain and Federated Learning (PPFP-BFL), a holistic architecture that synergistically integrates blockchain with federated learning. The framework's privacy is anchored by a novel Non-Interactive Functional Encryption (NIFE) scheme enhanced with SIMD technology, which secures model parameters with provable security and significantly reduced overhead. Concurrently, a novel incentive mechanism is established, transforming the blockchain from a passive ledger into an active arbitrator for fair exchange through automated, performance-based smart contracts. The framework's superiority and viability are validated by formal analysis and extensive experiments, which demonstrate a significant reduction in overhead against cryptographic-based solutions without sacrificing model accuracy.

Search and rescue (SAR) assumes primary focus during the post-disaster response phase. In recent years, the rapid advances in autonomous aerial vehicle (AAV) target detection technology have opened up new possibilities for SAR operations. However, the images captured by AAV exhibit considerable variation as they dynamically operate at different altitudes, posing challenges for rescue teams in identifying inconspicuous rescue targets. Furthermore, rescuers can hardly trust a detection model with an opaque decision-making process. To this end, we propose an explainable Region of Interest (RoI) attention-based AAV target detection network (RAXNet) capable of detecting small rescue targets with visual explanations. In this model, we first adopt path aggregation network (PANet) as the neck to extract features from different scales. Then, a RoIAttention module is designed to enhance the small target features while providing visual explanations. Specifically, we employ the RoIAlign and nonmaximum suppression methods to obtain region proposals of small targets in the low-level feature layer, followed by an attention-based feature enhancer to focus the extracted region proposals. By combining the enhanced features with the original ones, RAXNet can detect inconspicuous rescue targets and offer corresponding visual explanations, improving model credibility and facilitating rescue efficiency. Finally, we build an AAV visualization system to help rescuers assess disaster sites in real time. The effectiveness and explainability of the proposed method is demonstrated on the VisDrone DET benchmark and RescueNet datasets.

In complex electromagnetic environments, targets that need to be interfered with often possess high levels of concealment and anti-interference capabilities. Additionally, due to the dynamic characteristics of these targets, interference tasks must be conducted within strict time constraints to ensure interference effect. In this article, we adopt a reconnaissance-first approach for concealed targets. After detecting the accurate location of the target, we deploy autonomous aerial vehicles (AAVs) to interfere with the targets. First, we established a AAV swarm task scheduling optimization model after considering constraints, such as target threat range, priority of reconnaissance and interference tasks, interference task time, and AAV energy consumption. Meanwhile, we model the anti-interference capability of the target as a threat range. Second, we propose a nondominated sorting genetic algorithm based on distance in the solution space and a dynamic parent selection strategy (DPSNSGA-II) to solve AAV resource scheduling optimization problem. The diversity of the population is increased and the situation of falling into local optima is reduced by improving the parent individual selection strategy, mutation strategy, and elite solution retention mechanism. Finally, we construct two data sets of varying sizes to evaluate the quality of solution sets and the convergence performance of the proposed algorithm. The simulation results indicate that the proposed DPSNSGA-II algorithm has better result for population diversity and convergence compared to state-of-the-art algorithms.

The Industrial Internet of Things (IIoT) faces serious data privacy issues, such as the risk of data leakage during aggregation and transmission. However, existing studies rarely consider data privacy protection from the perspective of 5G core networks (CNs). This article proposes a federation chain-based data privacy protection system for the control plane in 5G CN to facilitate secure and decentralized communications between IIoT devices and other networks components, enhancing data integrity and confidentiality. Using XPRO instrument, 5G CN signaling storm simulation test platform, Free5GC, UERANSIM simulator, and Kali platform, the system simulates and generates realistic control plane data streams in IIoTs. To prevent unauthorized data access, we design an authentication algorithm based on the Bulletproofs zero-knowledge proof technique. Additionally, we implement a data encryption and decryption algorithm based on the Paillier partially homomorphic encryption for user privacy. We design a foundation model-based anomaly flow detection and analysis module to improve the security of the system for anomalous signaling flows. The feasibility and effectiveness of the system are validated on a 5G CN simulation testing platform, and experimental results show that the proposed approach ensures robust and scalable IIoT data privacy protection.

With the explosive growth in Industrial Internet of Things (IIoT) devices, the volume of multimedia data in the field of flexible manufacturing has also increased significantly in recent years, especially the vast amount of unlabelled image data. Image annotation provides machines with a more natural way to interact with users, enhancing the level of intelligence in IIoT flexible manufacturing. This article proposes a multifeature fusion multikernel learning image annotation method to tackle imbalanced label distribution, image weak labeling, and varying representational abilities of features. Initially, oversampling techniques with synthetic minority class samples address the influence of minority classes, while a label enhancer extends label vectors to overcome the influence of weak labeling. Subsequently, the integration of traditional visual features with deep features based on multikernel learning is investigated to enhance feature representation capability. This approach combines complementary information from multiple features, establishing intrinsic connections between images and annotated keywords. Experimental evaluations are conducted on three benchmark datasets, comparing our method with several classical methods. Evaluation results demonstrate that our proposed method captures semantic information more accurately and comprehensively. By effectively accomplishing automatic image annotation, our method can enhance human-machine-interaction to improve the level of intelligence in IIoT flexible manufacturing.

In response to the critical need for rapid post-disaster assessments, this article introduces an innovative application of artificial intelligence (AI) in unmanned aerial vehicles (UAVs) for disaster relief. A lightweight distributed learning algorithm (namely, YO-FR), is designed to enable multiple UAV agents to share and process environmental data, highlighting the importance of data and knowledge-empowered distributed learning. Moreover, we create a real-world mini-data set collected by UAVs for post-disaster road defects (mini-UPRDs), followed by a data enhancement technology to facilitate feature extraction and promote knowledge-driven learning. The viability of YO-FR is underscored by its enhanced detection precision and processing speed, as evidenced by its performance on the enhanced mini-UPRD data set, surpassing that of existing algorithms. By implementing AI algorithms on UAV platforms, this research offers a theoretical and practical foundation for the practical deployment of IUA in critical application areas, such as emergency management and disaster response.

The use of Unmanned Aerial Vehicles (UAVs) as flying base stations to support various tasks, such as data collection, machine learning (ML) model training, and wireless communication in Internet of Things (IoT) networks, has garnered significant attention in recent years. Nonetheless, several challenges have arisen in this context, including data privacy concerns and limited onboard computational and communication resources. These challenges make the direct transmission of raw data to a central server for training impractical. Moreover, UAV-based networks are susceptible to fluctuating channel conditions and the heterogeneous computing capabilities of IoT devices. Therefore, enhancing the reliability and efficiency of such networks is imperative. In this paper, we introduce a novel framework known as the Asynchronous Federated Framework for IoT-enabled UAV (AF3N) networks. AF3N enables local model training with subsequent parameter transmission to the Mobile Edge Computing (MEC) server. To further enhance learning efficiency, we incorporate a device selection strategy into the AF3N framework. Additionally, we employ a multi-agent Asynchronous Advantage Actor-Critic (A3C)-based joint resource allocation algorithm aimed at reducing latency and energy utilization within the Internet of UAVs (IoUAV) network. Through extensive simulations we comprehensively examine the efficacy and performance of our proposed framework.

With the development of artificial intelligence technology, UAVs have the ability to perceive the environment. UAV combined with target detection technology for road environment perception has received extensive attention. However, the complexity and variability of real-world road environments pose challenges for target detection. To address these challenges, we propose a uav small target detection algorithm AS-YOLOV5 with adaptive feature fusion and improved attention mechanism. In the feature extraction phase, AS-YOLOV5 employs soft pooling to bolster the feature extraction network, mitigating the loss of critical edge information of small targets inherent in standard down-sampling techniques. Our feature fusion method incorporates learnable parameters, effectively rebalancing feature layers to ensure small target information remains significant during the fusion process and is not obscured by large target features. The subspace attention module is optimized to enhance the representation of small target features while suppressing background interference. To ensure the detection branch captures essential shallow information about small objects, we introduce an additional feature extraction layer for feature fusion. Simulation results show that the proposed method outperforms the existing algorithms. AS-YOLOV5 achieved an mAP(mean Average Precision) of 56.36% on the BDD100K dataset and an impressive 93.33% on the KITTI dataset with an IOU (Intersection over Union) threshold of 0.5.

In intelligent transportation systems, efficient traffic management and population monitoring is attributed to the real-time scheduling of multiple Transportation Big Data (TBD) flows, which strongly supported risk assessment and control during the COVID-19 pandemic. However, as a complex and heterogeneous network, it is difficult to meet the priority and real-time requirements of multi-modal TBD flow scheduling. To improve the real-time performance of TBD flow scheduling, a scheme for Time-Triggered (TT) flow scheduling and Audio-Video Bridging (AVB) flow scheduling is proposed. First, an online scheduling method for TT flows (RFSD) is proposed, which uses Lion Swarm Optimization (LSO) algorithm for priority assignment and dynamic queues to adjust the scheduling order in real time. It ensures fairness in scheduling and effectively improves the utilization of time slot resources. Furthermore, an online scheduling method for AVB flows (RFSU) is proposed, which uses the Imperialist Competitive Algorithm (ICA) to construct the utility function for evaluating the scheduling value of AVB flows, effectively increasing the throughput of AVB flows. Finally, extensive experiments show that RFSD increases successful scheduling by 22% over the PAS algorithm. Compared to the TTA algorithm, RFSU achieves a 24% reduction in average delay and a 27% reduction in jitter.

Cluster analysis of ship trajectory data collected by Automatic Identification System (AIS) is an important method to study ship behavior patterns and discover traffic laws. Traditional clustering algorithms face the problems of trajectory similarity measurement, feature extraction and clustering parameter setting when dealing with a large number of AIS data. In this paper, an AIS trajectory clustering method based on unsupervised deep embedding is proposed. Using automatic encoder and deep clustering network, the data feature representation and clustering allocation are carried out simultaneously in low dimensional feature space, and iteratively optimizes clustering by minimizing Kullback - Leibler (KL) divergence. The experimental results show that the proposed algorithm can effectively cluster AIS trajectories and accurately extract the main route of ships in the water area.

In this paper, packet loss rate and frame rate control finction for multimedia communication systems under heterogeneous environment by the wired and the wireless networks is proposed. In our suggested system, as channel coding, FEC (Forward Error Correction) method with Reed-Solomon coding is introduced to reduce the packet error rate on the wireless network. On the other hand, a frame rate control function is introduced on the source host receive host and the BS (Base Station). When it becomes larger for the rate of packet loss or delay, frame rate is changed, a frame can be chosen and be transmitted. Thereby, It can maintain the throughput of End-to-End while the packet error rate is reduced to the accepted value.