The cognitive satellite-terrestrial network (CSTN) has emerged as a transformative architecture for enabling ubiquitous global connectivity, yet its broadcast nature and heterogeneous service demands pose critical security challenges against increasingly sophisticated wiretap threats. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted secure sparse code multiple access (SCMA) framework in CSTN, which aims to maximize the achievable secrecy rate by jointly optimizing the transmit beamforming, RIS reflection matrix, and SCMA codebook configuration while satisfying power constraints at the satellite and base station and meeting the quality-of-service demands of legitimate users. Specifically, a realistic RIS model incorporating coupled phase-amplitude constraints is considered for practical deployment scenarios. To solve the above non-convex optimization problem in dynamic environments, an intelligent decision-making mechanism based on a modified multi-agent two-delay deep deterministic (MMTD3) algorithm is developed to effectively decouple continuous beam control and discrete codebook selection, and provide a new paradigm for AI-driven cross-domain security optimization in CSTN. Simulation experiments demonstrate that the proposed framework outperforms existing benchmarks in key metrics including convergence, reward values, and secrecy rate, highlighting the framework’s potential for supporting ubiquitous security and massive heterogeneous service demands in CSTN.

As a key component of the space-air-ground integrated network (SAGIN), low Earth orbit (LEO) satellites aim to provide global coverage and reliable services under high-speed mobile conditions, which are critically challenged by severe Doppler effect and inherent broadcast security threats. To address these issues, this paper investigates a multi-reconfigurable intelligent surface (RIS)-assisted orthogonal time frequency space (OTFS) downlink transmission system, where a LEO satellite serves multiple information receivers and potential eavesdroppers acting as energy receivers via simultaneous wireless information and power transfer (SWIPT). By jointly optimizing multi-dimensional resource variables, such as transmit beamforming and RIS reflection coefficients of the spatial domain, and the symbol scheduling matrix of the time-frequency domain, this paper aims to maximize the sum secrecy rate while satisfying constraints on satellite transmit power, the legitimate users’ quality of service, and energy-harvesting requirements. Given the high-dimensional, non-convex, and NP-hard nature of this problem, we develop an enhanced actor-critic deep reinforcement learning (DRL) framework. The core innovation lies in designing an episodic return-prioritized experience selection mechanism with online mixing, which significantly improves the sampling efficiency and policy stability by intelligently selecting training data. Simulation results demonstrate that the proposed approach outperforms existing schemes in achieving a higher sum secrecy rate, providing a practical and highly efficient resource scheduling solution for building secure and reliable next-generation SAGIN.

To address the critical security challenges posed by the inherent broadcasting nature and heterogeneous service demands in cognitive satellite-terrestrial networks (CSTN), this paper presents a groundbreaking framework that integrates reconfigurable intelligent surfaces (RIS) with sparse code multiple access (SCMA). This framework aims to maximize the achievable secrecy rate by jointly optimizing transmit beamforming, the RIS reflection matrix, and SCMA codebook configurations, while adhering to power constraints and the quality-of-service requirements of legitimate users. To tackle the non-convex optimization problem in complex environments, we develop an intelligent decision-making mechanism based on a modified multi-agent two-delay deep deterministic (MMTD3) algorithm, which introduces a breakthrough mechanism by decoupling continuous beam control from discrete codebook selection, offering a new paradigm for AI-driven cross-domain security optimization in CSTN. Simulation results demonstrate that the proposed framework significantly outperforms existing benchmarks, verifying its potential in supporting wireless endogenous security and meeting massive heterogeneous service demands in CSTN.

In the context of future 6G networks, reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) are emerging as pivotal technologies for enhancing signal quality and eliminating coverage blind spots. This paper addresses the issue of secure and resilient transmission in RISassisted NOMA systems. Specifically, the base station transmits private signals to multiple legitimate users while dealing with the threat of potential eavesdropping. To model this challenge, we optimize the beamforming vectors and the RIS phase-shift matrix to maximize the sum secrecy rate while satisfying the user quality of service (QoS) requirements and the power constraints of the base station. Since the problem involves high-dimensional variables and non-convex objective functions, it is difficult to be solved by traditional optimization methods. Therefore, the twin delayed deep deterministic policy gradient algorithm (TD3) based on deep reinforcement learning (DRL) is proposed in this paper to effectively address the complexity of the original problem. Numerical results show that the proposed scheme exhibits satisfactory performance in improving communication security, transmission efficiency, and resistance to channel errors.

PHY-layer spoofing attack is a potential critical issue in wireless network communication security, which could lead to catastrophic consequences for critical mission and applications, especially in Industrial Internet of Things scenarios with enormous number of devices. In this paper, we propose a novel spoofing attack detection scheme exploiting Channel State Information (CSI) phase difference. Firstly, we establish a mapping between CSI phase difference and the location of wireless communication devices to achieve the goal of spoofing attack detection. Due to the stable property of CSI phase difference, we convert CSI phase difference into heatmaps for subsequent training of the neural network model. Then we propose Wasserstein generative adversarial network and Encoder (WGAN-Encoder) deep-learning-based model in the scheme. This model utilizes discriminator feature residual error and image reconstruction error to get anomaly score for spoofing attack detection. This model overcomes the limitations of traditional detection methods on prior knowledge the attacker’s real CSI under real communication scenarios. Finally, we carry out extensive experimental evaluations about the detection performance and robustness of the proposed scheme based on data collected in time-varying scenarios. The results have successfully demonstrated that the proposed scheme exhibits outstanding performance.