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.

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 flexible deployment of Unmanned Aerial Vehicle (UAV) swarms holds significant potential for low-altitude economy, but their communication security is severely threatened by malicious jamming. Generally, existing anti-jamming methods often overlook multi-user interference in swarm scenarios and fail to exploit the full potential of Intelligent Reflecting Surface (IRS) architectures. To solve the above challenges, we propose for the first time an anti-jamming framework for UAV swarm communications assisted by a Hybrid-IRS-assisted UAV (H-UAV). We jointly optimize the H-UAV’s trajectory, the hybrid IRS’s beamforming and active/passive element allocation of IRSs, and Non-Orthogonal Multiple Access (NOMA) communication strategy under imperfect jammer Channel State Information (CSI), to maximize average system transmission rate while minimizing communication energy consumption. To handle the formulated highly-coupled non-convex problem, we decompose it into three sub-problems. Specifically, we employ Successive Convex Approximation (SCA) to optimize the H-UAV’s trajectories. The IRS beamforming and element allocation are then transformed into a semi-definite programming problem by a designed penalty-based approach. Finally, the NOMA decoding order and power allocation are optimized via a dynamic ordering scheme and an SCA-based algorithm. Compared to existing representative schemes, the proposed framework can achieve higher average transmission rates and lower energy consumption.

Over-the-Air Federated Edge Learning (OTA-FEEL) has emerged as a promising paradigm for collaborative AI model training across heterogeneous edge devices. Despite its advantages in communication efficiency and privacy preservation, OTA-FEEL faces critical challenges, including channel fading, energy constraints of edge devices, and non-i.i.d data distributions. This paper is the first to investigate a joint impact of local data distribution heterogeneity and transmission distortion on model convergence of OTA-FEEL. Accordingly, we analyze the gap between global expected and optimal losses, and formulate the gap minimization problem under long-term energy consumption constraints. To solve this problem, we propose an energy-aware alternating resource allocation algorithm based on Lyapunov optimization framework, jointly addressing transmit power control and device sampling rate selection. Specifically, we transform the non-convex problem based on inverse convex optimization. Then, we employ first-order Taylor expansion to linearize the non-convex constraint, and also develop an iterative framework based on block coordinate descent and successive convex approximation to enable rapid convergence. Extensive simulations under three types of non-i.i.d data distributions validate the effectiveness of the proposed EARA algorithm, which consistently outperforms representative algorithms by achieving test accuracy approaching the theoretical upper bound, while maintaining significantly low energy consumption.

Ground-air cooperative package distribution is a promising delivery method, especially during Corona virus Disease 2019. It can extend the coverage of vehicles by exploring the flexibility of unmanned aerial vehicles (UAVs), expand the distribution of vehicles and reduce carbon emissions. Most existing studies focus on their trajectory optimization, while often overlooking their coordination for global information, and the complexity and reliability of collaborative delivery problem. To address the above issues, we first formulate an optimization problem to minimize the service cost of both UAVs and vehicles. To ensure service reliability, the constraints of UAVs during takeoff, service, and landing phases are comprehensively considered. We then propose a lightweight reinforcement learning solution to minimize the flight distance of UAVs and the number of required vehicles. Finally, theoretical analysis and performance evaluations show that compared with other representative algorithms, the designed algorithm has advantages in terms of robustness, effectiveness and stability.