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With the rapid development of 6G networks, anomaly detection in network edge intelligence faces significant challenges in system interpretability and trustworthiness. Although machine learning-based methods improve detection performance, their black-box nature limits reliable cybersecurity decision support. To address this, we propose a novel framework integrating causal inference with LSTM networks. Our approach first applies Random Fourier Feature transformation to eliminate nonlinear feature correlations—a prerequisite for valid causal analysis. We then quantify feature-specific causal effects using sample-weighted adjustments to ensure model stability. Furthermore, Generative Adversarial Networks generate high-quality minority-class samples to augment training data, enhancing anomaly detection accuracy. Experimental validation on two large-scale datasets demonstrates a 33.7% improvement in explainability and a 68% reduction in root-cause localization time. This work establishes a new cybersecurity paradigm for 6G edge intelligence through causal reasoning.

Specific emitter identification (SEI) methods based on deep learning (DL) have effectively addressed complex, multi-dimensional signal recognition tasks by leveraging deep neural networks. However, this advancement introduces challenges such as model parameter redundancy and high feature dimensionality, which pose limitations for resource-constrained (RC) edge devices, especially in Internet of Things (IoT) applications. To tackle these problems, we propose an RC-SEI method based on efficient design and model compression. Specifically, for efficient design, we have developed a lightweight convolution network (LCNet) that aims to balance performance and complexity. Regarding model compression, we introduce sparse regularization techniques in the fully connected (FC) layer, achieving over 99% feature dimensionality reduction. Furthermore, we have comprehensively evaluated the proposed method on public automatic-dependent surveillance-broadcast (ADS-B) and Wi-Fi datasets. Simulation results demonstrate that our proposed method exhibits superior performance in terms of both recognition accuracy and model complexity. Specifically, LCNet achieved accuracies of 99.40% and 99.90% on the ADS-B and Wi-Fi datasets, respectively, with only 33,510 and 33,544 parameters. These results highlight the feasibility and potential of our proposed RC-SEI method for RC scenarios.

The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of RF identification techniques, which could be useful in forensic processes.

Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from MCC > 0.9 (excellent) down to MCC < 0.05 (random guess), indicating that single-channel models may not maintain performance across all channels used by the transmitter in realistic operation. We proposed a training data selection technique to create multi-channel models which outperform single-channel models, improving the cross-channel average MCC from 0.657 to 0.957 and achieving frequency channel-agnostic performance. When evaluated in the presence of noise, multi-channel discriminant analysis models showed reduced performance, but multi-channel neural networks maintained or surpassed single-channel neural network model performance, indicating additional robustness of multi-channel neural networks in the presence of noise.

Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context; and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection.

To improve communication stability, more wireless devices transmit multi-modal signals while operating. The term ‘modal’ refers to signal waveforms or signal types. This poses challenges to traditional specific emitter identification (SEI) systems, e.g., unknown modal signals require extra open-set mode identification; different modes require different radio frequency fingerprint (RFF) extractors and SEI classifiers; and it is hard to collect and label all signals. To address these issues, we propose an enhanced SEI system consisting of a universal RFF extractor, denoted as multiple synchrosqueezed wavelet transformation of energy unified (MSWTEu), and a new generative adversarial network for feature transferring (FTGAN). MSWTEu extracts uniform RFF features for different modal signals, FTGAN transfers different modal features to a recognized distribution in an unsupervised manner, and a novel training strategy is proposed to achieve emitter identification across multi-modal signals using a single clustering method. To evaluate the system, we built a hybrid dataset, which consists of multi-modal signals transmitted by various emitters, and built a complete civil air traffic control radar beacon system (ATCRBS) dataset for airplanes. The experiments show that our enhanced SEI system can resolve the SEI problems associated with crossing signal modes. It directly achieves 86% accuracy in cross-modal emitter identification using an unsupervised classifier, and simultaneously obtains 99% accuracy in open-set recognition of signal mode.

In the context of next-generation wireless communication and heterogeneous Internet of Things (IoT) systems, the security of IoT based on cryptographic mechanisms and security protocols presents significant vulnerabilities. The radio frequency fingerprinting (RFF) method based on the physical layer of signals is considered an effective and reliable solution to address these issues. RFF identification utilizes the subtle differences in radio frequency signals generated by emitters to distinguish between different individuals, making it difficult to clone or forge. RFF identification extracts features from collected radio signals through signal processing to achieve specific emitter identification (SEI) tasks, with RFF features being the key elements for realizing SEI. To promote research development in this field, this paper comprehensively reviews the identification methods of RFF from the perspectives of statistical features and deep learning features. Firstly, this paper introduces the fundamental theory of RFF, including its origin, formation mechanism, properties, an analysis of its development trends, and a compilation of available datasets. Secondly, based on the general model of emitter identification, it reviews current RFF identification methods based on both statistical and deep learning features and conducts a comparative study of these two approaches. Finally, it highlights several development challenges and potential research directions in the field of intelligent RFF identification, aiming to guide future research and applications in this domain.

Radio Frequency Fingerprinting (RFF) would be a promising Physical Layer Security (PLS) solution for the Internet of Things (IoT) that requires robust, low-overhead security techniques. However, practical implementation of RFF may pose challenges, in particular, performance instability and ethical-regulatory conflicts. Based on authors’ previous research, this paper elaborates these challenges in potential deployment of a resilient and compliant RFF system. First, we analytically show how hardware-induced feature drift, primarily driven by device aging and temperature variations, degrades RFF performance. We then critically survey existing temperature variation and aging models, one of which is being studied by one of the authors’ research team. We look into this from a purely hardware-design perspective, and then compensation methods for an RFF perspective. This reveals a significant gap: current techniques are insufficient to maintain the long-term, high-accuracy RFF for real-world IoT security requirements. Finally, we introduce inherent privacy risks by enabling device tracking. This property conflicts with General Data Protection Regulation (GDPR) mandates, raising significant regulatory challenges and privacy risks. Overall, this work highlights the key technical and legal challenges that must be addressed for RFF to evolve into a robust, privacy-compliant and deployable security primitive for IoT and future wireless systems.

As IoT (internet of things) devices grow in prominence, safeguarding them from cyberattacks is becoming a pressing challenge. To bootstrap IoT security, device identification or authentication is crucial for establishing trusted connections among devices without prior trust. In this regard, radio frequency fingerprinting (RFF) is gaining attention because it is more efficient and requires fewer computational resources compared to resource-intensive cryptographic methods, such as digital signatures. RFF works by identifying unique manufacturing defects in the radio circuitry of IoT devices by analyzing over-the-air signals that embed these imperfections, allowing for the identification of the transmitting hardware. Recent studies on RFF often leverage advanced classification models, including classical machine learning techniques such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), as well as modern deep learning architectures like Convolutional Neural Network (CNN). In particular, CNNs are well-suited as they use multidimensional mapping to detect and extract reliable fingerprints during the learning process. However, a significant limitation of these approaches is that they require large datasets and necessitate retraining when new devices not included in the initial training set are added. This retraining can cause service interruptions and is costly, especially in large-scale IoT networks. In this paper, we propose a novel solution to this problem: RFF using Siamese networks, which eliminates the need for retraining and allows for seamless authentication in IoT deployments. The proposed Siamese network is trained using in-phase and quadrature (I/Q) samples from 10 different Software-Defined Radios (SDRs). Additionally, we present a new algorithm, the Similarity-Based Embedding Classification (SBEC) for RFF. We present experimental results that demonstrate that the Siamese network effectively distinguishes between malicious and trusted devices with a remarkable 98% identification accuracy.

Six degrees-of-freedom (6D) object pose estimation plays an important role in pattern recognition of fields such as robotics and augmented reality. However, there are issues with low accuracy and real-time performance of 6D object pose estimation in complex scenes. To address these challenges, in this article, RFF-PoseNet (a 6D object pose estimation network based on robust feature fusion) is proposed for complex scenes. Firstly, a more lightweight Ghost module is used to replace the convolutional blocks in the feature extraction network. Then, a pyramid pooling module is added to the semantic label branch of PoseCNN to fuse the features of different pooling layers and enhance the network’s ability to capture information about objects in complex scenes and the correlations between contextual information. Finally, a pose regression and optimization module is utilized to further improve object pose estimation in complex scenes. Simulation experiments conducted on the YCB-Video and Occlusion LineMOD datasets show that the RFF-PoseNet algorithm can strengthen the correlation of features between different levels and the recognition ability of unclear targets, thereby achieving excellent accuracy and real-time performance, as well as strong robustness.