The widespread integration of Artificial Intelligence (AI) in sixth-generation Internet of Things (6G-IoT) applications, introduces significant challenges for ensuring the trustworthy and dependability of AI models. The "black-box" characteristic of numerous Deep Neural Networks (DNNs) creates a notable obstacle for confirming their safety in intricate, ever-changing environments. Consequently, there is a need for extensive testing, requiring the gathering and labeling of a large number of test cases, a process that is both time-intensive and resource-consuming. While previous studies have adapted neuron coverage criteria for steering test case generation in DNNs. Yet, these criteria are white-box measures requiring access to model states and presenting their practical limitations. Conversely, black-box metrics, which focus on outputs, present a more feasible approach. Among these, black-box diversity metrics evaluate model robustness by generating diverse test cases, eliminating the need for internal model details. This paper presents a test case generation framework centered on diversity, known as BDTest. BDTest enhances test adequacy through five stages: (1) Mapping feature vectors extracted from an initial set of seed images onto a low-dimensional manifold utilizing UMAP; (2) Detecting sparse regions using DBSCAN; (3) Sampling key points from these regions via Latin Hypercube Sampling; (4) Reconstructing latent features and generating new images through ICA and GAN inversion; and (5) Measuring the diversity of the generated set using metrics such as the Log-Determinant. Experiments demonstrate that BDTest significantly improves test set diversity and error detection performance, achieving error rates of 59.36%, 59.76%, and 67.03% on VGG19, DenseNet121, and MobileNetV2, respectively, outperforming DeepXplore by an average of 12.43% and DLFuzz by 9.95% across all tested models. When retrained with the generated test cases, the model demonstrated improved accuracy on the original test set, alongside a significant enhancement in accuracy on the natural adversarial test set.

The widespread integration of artificial intelligence (AI) in next-generation communication networks poses a serious threat to data privacy while achieving advanced signal processing. Eavesdroppers can use AI-based analysis to detect and reconstruct transmitted signals, leading to serious leakage of confidential information. In order to protect data privacy at the physical layer, we redefine covert communication as an active data protection mechanism. We propose a new parasitic covert communication framework in which communication signals are embedded into dynamically generated interference by generative adversarial networks (GANs). This method is implemented by our CDGUBSS (complex double generator unsupervised blind source separation) system. The system is explicitly designed to prevent unauthorized AI-based strategies from analyzing and compromising signals. For the intended recipient, the pretrained generator acts as a trusted key and can perfectly recover the original data. Extensive experiments have shown that our framework achieves powerful covert communication, and more importantly, it provides strong defense against data reconstruction attacks, ensuring excellent data privacy in next-generation wireless systems.

This paper proposes a parasitic covert communication framework for unmanned aerial vehicle (UAV), which embeds communication signals into dynamically adaptive interference. Legitimate receivers can extract target signals from parasitic interference, while the eavesdropper fails to accomplish. In a single-channel scenario, we propose a complex dual-generator unsupervised blind source separation (CDGUBSS) method, which implements a novel dual-phase adversarial architecture, namely, signal pre-training phase and adversarial separation phase. The former phase employs complex-valued generative adversarial networks (GANs) to learn latent representations of communication signals, while the latter one introduces a dual-generator dynamic learning mechanism to separate communication signal from the parasitic signals. Extensive experiments demonstrate the superiority of our proposed scheme, which validate its capability to enable robust parasitic covert communication.

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.

Cell-free massive MIMO (CF mMIMO) systems overcome inter-cell interference, enhancing overall communication rates for next-generation networks. However, the pilot contamination exacerbates channel estimation errors and the complex connectivity makes it difficult to deal with resource allocation optimization problem. In this paper, we investigate the robust beamforming problem under channel uncertainty with the goal of improving the minimum quantile rate. Specifically, we introduce hypergraph neural network (HGNN) into the wireless resource allocation of CF mMIMO ststems for the first time, leveraging hypergraph modeling to capture the many-to-many relationships between Access Points (APs) and User Equipments (UEs). Furthermore, we significantly reduce the search space of the optimization problem by applying optimal interference suppression beamforming theory. In order to soften the sorting process, we adopt the Monte Carlo sampling strategy for data augmentation. Simulation results demonstrate that the proposed algorithm outperforms conventional schemes, achieving 14.1% performance gain and converging more than twice as fast as the state-of-the-art machine learning models.

Rapid iterations and updates in both software and hardware, along with significant advancements in communication technology, have given rise to the concepts of symbiotic Internet of Things (IoT) and ubiquitous interconnectivity, providing strong evidence for the flourishing development of the Internet of Things. However, the limited resources and computing capabilities, along with the heterogeneity of deployment environments, make symbiotic IoT devices more susceptible to security threats and operational issues. Intermittent failures are especially prevalent in the symbiotic IoT, leading to more significant risks for devices. In this paper, we present an IFDGAT-LSTM (Intermittent Fault Diagnosis Based on Long Short-Term Memory and Graph Attention Network) framework for diagnosing intermittent failures in wireless sensing devices within the symbiotic IoT. The framework is based on a graph neural network and takes into account not only the time series characteristics of symbiotic IoT devices but also their deployment topology. By incorporating both aspects, we achieve more accurate diagnostics of intermittent failures in the symbiotic IoT, thus enhancing its reliability. Firstly, we propose the concept of a quasi-dynamic graph based on the variations in the topology within the symbiotic IoT. Subsequently, we introduce an intermittent failure diagnosis framework that combines a graph neural network to identify intermittent failure nodes within the quasi-dynamic graph. Finally, we performed experiments on the WADI symbiotic IoT dataset to evaluate the performance of our model in diagnosing intermittent failure nodes. We used the precision, recall, and F1 score metrics for assessment. The experimental outcomes show that our proposed model, IFDGAT-LSTM, achieves an Precision of 99.58% in diagnosing intermittent failure nodes. This highlights the strong performance and efficacy of the IFDGAT-LSTM model.

The rapid proliferation of Internet of Things (IoT) systems has introduced complex networks of interconnected devices, computational resources, and web-based communication infrastructure. Privacy protection in IoT data routing is critical to enabling secure deployment of large language models (LLMs) for processing distributed sensor data, user queries, and device-generated content. However, IoT environments inherently involve heterogeneous devices, dynamic network topologies, and resource-constrained nodes, complicating the design of privacy-preserving routing mechanisms that simultaneously ensure reliability across diverse communication layers. To address these challenges, we propose an innovative FtPR (Fault-tolerant Privacy Routing) model based on secure multiparty computing mechanism, which enables secure and efficient data fusion and transmission in IoT networks. FtPR establishes a novel connection between IoT device clusters and data center network architecture AQDNn routers, leveraging the hierarchical architecture of AQDNn to construct completely independent spanning trees (CIST). By exploiting the non-overlapping paths between nodes in distinct CISTs, FtPR achieves fault-tolerant routing while maintaining privacy guarantees. Building on this framework, we introduce a secure multiparty computing mechanism to perturb link weights in the AQDNn. This ensures that link weights across different CISTs adhere to constrained ranges, preventing adversarial inference of routing paths. Each node operates with localized knowledge of its connected link weights, eliminating the need for global network visibility. Consequently, even if malicious actors compromise one or multiple nodes, they cannot reconstruct end-to-end communication paths, thereby preserving route anonymity. Experimental results demonstrate that FtPR improves IoT network performance and security, reducing misclassification rates and marginal release score compared to state-of-the-art methods.

Constructing digital twin (DT) models of user equipments (UEs) efficiently is essential for enabling real-time monitoring of UEs, providing crucial support for optimizing the operation of Social Internet of Things (SIoT) systems. However, UE heterogeneity and UE mobility concerns impede the DT deployment in SIoT. In this article, we propose a real-time DT deployment (RDTD) scheme for SIoT systems, where the heterogeneous DT modeling and the DT migration are achieved based on personalized federated learning (PFL) ideas. Specifically, we decompose the DT model into the global generalization layers and the personalization layers, based on which we propose a hierarchical PFL (HPFL)-based DT model construction mechanism. The mechanism constructs customized DT models for heterogeneous UEs through a two-stage model parameter update process, involving end-edge-center collaboration training of all parameters and fine-tuning of the personalization layer parameters. Second, based on the above mechanism for DT model construction, a low-latency DT model parameter migration algorithm is proposed. This algorithm ensures real-time interaction by migrating only the personalized layer parameters of the DT model and reconstructing the DT model. Lastly, numerical experiments verify the effectiveness of the RDTD scheme, improving modeling accuracy by 13.91%, 41.06%, and 135.35% compared to the three baselines. Additionally, our proposed scheme significantly reduces interaction latency by 29.93% compared to the baseline.

This paper presents a Lora-enabled GPU-based CubeSat Neural-Network Real-Time Object Detection with hyperparameter optimization is presented. When number of epochs increased from 10 to 50 and learning rate tuned to 0.00104, the validation loss improved to 0.018 and training object loss improved to 0.042. Model mean average precision mAP_0.5 is improved to 0.986 and the precision is improved to 98.9%. Epoch and learning rate are traded-off to optimize model accuracy performance. The Lora-enabled CubeSat onboard transceiver provides long range onboard sensors readings providing spaced-based IoT application capability.

This paper presents the design of a fully-integrated RF low noise amplifier with active inductor linearizer. The proposed circuit achieves 16 dBc of third order intermodulation IM3 distortion cancellation at 1.9 GHz with 8 dB third-order intercept point IP3 improvement. The proposed circuit utilize an active inductor linearizer to improve linearity of the circuit and offering tunability. The simulation results show an overall proposed circuit peak gain is 14.5 dB and the minimum noise Figure is 0.75 dB at 1.9 GHz frequency with power consumption of 7.4 mA.

In this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method.