In next-generation non-terrestrial network environments, the increasing risk of detection by unauthorized observers has motivated extensive research on covert communication approaches that minimize the probability of detection. In particular, jamming-assisted cooperative covert communication has attracted significant attention as an effective approach to simultaneously ensure communication performance and security, leading to growing interest in cooperative architectures among heterogeneous platforms. This study investigates covert communication in Low Earth Orbit (LEO) satellite–unmanned aerial vehicle (UAV) cooperative networks, where the LEO satellite serves a legitimate user, while the UAV acts as a cooperative jammer to enhance covertness. A network that integrates a LEO satellite with wide service coverage and a UAV with high mobility offers flexible support for covert communication in diverse environments. However, the problem of optimally allocating power between the LEO satellite and UAV while satisfying the covert communication constraint inherently exhibits a non-convex structure, which commonly necessitates a discretized grid-search baseline over feasible candidate combinations. As the number of candidates increases, this approach suffers from rapidly increasing computational complexity. To address this computational burden, this study proposes a machine-learning (ML)–based power-allocation scheme. The proposed ML model leverages key channel-related and covertness-related features to efficiently select an effective pair of power scaling factors, while significantly reduced computational complexity. Simulation results demonstrate that the proposed scheme achieves comparable average covert rate to that of the discretized grid-search baseline while requiring substantially lower computational complexity. These results further indicate that the proposed scheme enables low-latency and efficient power control in LEO satellite–UAV cooperative networks. Finally, future work will extend the proposed scheme to more complex multi-LEO satellite–UAV cooperative scenarios through joint optimization of additional system parameters.

As the density of wireless networks increases globally, the vulnerability of overlapped dense wireless communications to interference by hidden nodes and denial-of-service (DoS) attacks is becoming more apparent. There exists a gap in research on the detection and response to attacks on Medium Access Control (MAC) mechanisms themselves, which would lead to service outages between nodes. Classifying exploitation and deceptive jamming attacks on control mechanisms is particularly challengingdue to their resemblance to normal heavy communication patterns. Accordingly, this paper proposes a machine learning-based selective attack mitigation model that detects DoS attacks on wireless networks by monitoring packet log data. Based on the type of detected attack, it implements effective corresponding mitigation techniques to restore performance to nodes whose availability has been compromised. Experimental results reveal that the accuracy of the proposed model is 14% higher than that of a baseline anomaly detection model. Further, the appropriate mitigation techniques selected by the proposed system based on the attack type improve the average throughput by more than 440% compared to the case without a response.

The Internet of Things (IoT) is extensively applied across various industrial domains, such as smart homes, factories, and intelligent transportation, becoming integral to daily life. Establishing robust policies for managing and governing IoT devices is imperative. Secure authentication for IoT devices in resource-constrained environments remains challenging due to the limitations of conventional complex protocols. Prior methodologies enhanced mutual authentication through key exchange protocols or complex operations, which are impractical for lightweight devices. To address this, our study introduces the privacy-preserving software-defined range proof (SDRP) model, which achieves secure authentication with low complexity. SDRP minimizes the overhead of confidentiality and authentication processes by utilizing range proof to verify whether the attribute information of a user falls within a specific range. Since authentication is performed using a digital ID sequence generated from indirect personal data, it can avoid the disclosure of actual individual attributes. Experimental results demonstrate that SDRP significantly improves security efficiency, increasing it by an average of 93.02% compared to conventional methods. It mitigates the trade-off between security and efficiency by reducing leakage risk by an average of 98.7%.