The industrial Internet of Things (IIoT) requires robust intrusion detection systems (IDS) to ensure critical business continuity. However, existing solutions suffer from high false-positive rates, difficulty in interpreting, and cross-domain generalization. In particular, they fail to reliably infer context-dependent causal links from host logs under global graph-level context. To address these issues, we propose causal counterfactual graph-based IDS (CCG-IDS), an IIoT-focused interpretable graph neural network IDS based on conformal calibration and counterfactual reasoning. This system implements a unified provenance subgraph detection paradigm, and employs conformal anomaly detection and counterfactual reasoning to provide calibrated alerts and interpretable outputs. We also estimate predictive uncertainty via Fisher information to quantify decision confidence. We introduce a discrete counterfactual explainer with a counterfactual destructiveness score (CDS) to extract a minimal decision-critical evidence chain, and use this evidence to generate structured analyst-ready security reports. Experiments on real-world industrial log datasets, including the windows event log EVTX (Windows XML Event Log) and DARPA OpTC (Operationally Transparent Cyber) datasets, demonstrated that CCG-IDS achieved an$F1$score of 92% and a near-zero false-positive rate, outperforming other state-of-the-art methods.

The accurate imputation of missing load data in building energy consumption is essential for optimizing energy management and scheduling in Internet of Things (IoT)-based smart energy management systems. However, in real-world applications, building load data often suffers from the issue of missing critical samples due to IoT device failures and maintenance. To address this problem, we propose an effective scheme by designing a load data augmentation model named the DAM based on deep neural networks. In the DAM, the partial missing data are generated in each round, followed by stacking with the semi-dataset to perform a new generation round. After several rounds, the missing critical load data are recovered with high precision. A building load dataset collected from a real IoT-based energy-efficiency management system is used for evaluation in this work. Experimental results demonstrate that the proposed scheme can effectively replenish the missing critical data and exhibit excellent stability. Additionally, we compare the prediction performance of the DAM approach with other comparison methods. The results show that our proposed approach outperforms the comparison methods, achieving the highest R2 score of 0.963. Hence, the DAM approach presents an effective solution for addressing the problem of missing critical data in IoT-based smart energy management systems, which is vital for optimizing energy dispatch.

The Internet of Things (IoT) connects physical devices to the Internet via open communication protocols. Malicious actors can exploit vulnerabilities to steal data or manipulate critical IoT settings, so there is a need for strong security measures. We propose an improved real-time intrusion detection system (IDS) called the real-time credibility system (RTCS), which utilizes traffic statistics and authentication analysis to compute credibility. RTCS performs the authentication process by utilizing elliptic curve encryption and decryption operations, basic symmetric encryption, and hash functions. This process enables anonymous mutual authentication between IoT devices. Subsequently, RTCS accesses sparsified user history data and introduces flexibility in calculating user credibility by employing an adapted secondary paradigm combined with preset “tolerance parameters,” which serve as optimal thresholds for classifying different users. When a normal user violates regulations, their credibility decreases by a specified degree. If a high-risk user commits another violation, RTCS cannot tolerate it, leading to a rapid decline in their credibility. RTCS implements diversion measures and provides assisted decision scores for different users. Experimental results demonstrate that our method achieves an F1-score of 0.9707 and an area under the curve score of 0.9535. Compared to other works, RTCS exhibits superior performance and proactivity.

Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display, entertainment, social, and trading. It integrates the most fundamental technologies, such as Blockchain , Interaction, Games, Artificial Intelligence, Networks, and the Internet of Things , named BIGANT. Interaction technologies are significant to allow users to interact with virtual entities in physical environments via sensors, such as AR, MR, and VR. However, there are still great challenges regarding how to access the metaverse in a more intelligent, faster, and effective way, especially in capturing human positions and activities. Intelligent wireless sensing technology, integrating AI , can serve as an intelligent, flexible, non-contact way to access the metaverse and expedite the establishment of a bridge between the real physical world and the metaverse. Hence, this paper elaborates on the existing work and discusses potential important trends and hotspots in wireless sensing, especially localization, activity recognition, and pattern analysis. After that, we discussed how intelligent wireless sensing will evolve in the metaverse, together with current challenges and open issues in this topic. Through this overview, we wish readers can better understand how intelligent wireless sensing accelerates the accessing to metaverse and the insights behind the wireless sensing in the metaverse.

The Internet of Things (IoT) has evolved into a global platform dramatically facilitating human life through intelligent services. It is straightforward for people to access smart devices through IoT. However, the easy accessibility of IoT devices has also led to unprecedented security challenges for the IoT. To ensure the security of the basic structure of IoT, we need to establish a security barrier that can filter malicious access to IoT devices and achieve the integration of intrusion detection systems (IDSs) with intelligent gateways. This article establishes threat models of Denial of Service, Replay, man-in-the-middle, and Loophole attacks based on statistical flow characteristics and identity authentication. It uses supervised learning to obtain the credibility index to protect the IoT system. We use the Django framework to verify identity authorization information, the decision tree to determine request attributes, and the real-time status feedback from IoT devices to perform a risk assessment on the current user by precalculating the importance ratio (Ir), the maximum credibility index$(P_{\mathrm {max}})$, and the minimum credibility index$(P_{\mathrm {min}})$. With administrator verification, we conduct a convergence analysis to obtain user attributes. The experimental results show that our approach achieves a recognition accuracy of 94.7%.

Medical image processing plays an important role in the interaction of real world and metaverse for healthcare. Self-supervised denoising based on sparse coding methods, without any prerequisite on large-scale training samples, has been attracting extensive attention for medical image processing. Whereas, existing self-supervised methods suffer from poor performance and low efficiency. In this paper, to achieve state-of-the-art denoising performance on the one hand, we present a self-supervised sparse coding method, named the weighted iterative shrinkage thresholding algorithm (WISTA). It does not rely on noisy-clean ground-truth image pairs to learn from only a single noisy image. On the other hand, to further improve denoising efficiency, we unfold the WISTA to construct a deep neural network (DNN) structured WISTA, named WISTA-Net. Specifically, in WISTA, motivated by the merit of the$l_{p}$-norm, WISTA-Net has better denoising performance than the classical orthogonal matching pursuit (OMP) algorithm and the ISTA. Moreover, leveraging the high-efficiency of DNN structure in parameter updating, WISTA-Net outperforms the compared methods in denoising efficiency. In detail, for a 256 by 256 noisy image, the running time of WISTA-Net is 4.72 s on the CPU, which is much faster than WISTA, OMP, and ISTA by 32.88 s, 13.06 s, and 6.17 s, respectively.

Abstract Federated generative adversarial networks are designed to collaborate across the communication and privacy-constrained edge servers participating in training. However, in the Internet of Things scenario, local updates uploaded by edge servers can lead to the risk of privacy breaches. Gradient-sanitized-based approaches can transmit sanitized sensitive data with strict privacy guarantees, but gradient clipping and perturbation severely degrade convergence performance. In this paper, our proposed algorithm enhances the privacy of terminated raw data through differential privacy before it is transmitted to the edge server. The edge server trains the local generator and discriminator using the perturbed data, which provides privacy guarantees for the gradient attack on the FedGAN without compromising the gradient accuracy. The results of the experimental evaluation show that the algorithm generates images with slightly better quality than that generated by the gradient-sanitized-based approaches while maintaining privacy.

Device-free localization (DFL) locates targets without equipping with wireless devices or tag under the Internet-of-Things (IoT) architectures. As an emerging technology, DFL has spawned extensive applications in the IoT environment, such as intrusion detection, mobile robot localization, and location-based services. Current DFL-related machine learning (ML) algorithms still suffer from low localization accuracy and weak dependability/robustness because the group structure has not been considered in their location estimation, which leads to an undependable process. To overcome these challenges, we propose in this work a dependable block-sparse scheme by particularly considering the group structure of signals. An accurate and robust ML algorithm named block-sparse coding with the proximal operator (BSCPO) is proposed for DFL. In addition, a severe Gaussian noise is added in the original sensing signals for preserving network-related privacy as well as improving the dependability of the model. The real-world data-driven experimental results show that the proposed BSCPO achieves robust localization and signal-recovery performance even under severely noisy conditions and outperforms state-of-the-art DFL methods. For single-target localization, BSCPO retains high accuracy when the signal-to-noise ratio exceeds -10 dB. BSCPO is also able to localize accurately under most multitarget localization test cases.

Driven by the advanced technologies of vehicular communications and networking, the Internet of Vehicles (IoV) has become an emerging paradigm in smart world. However, privacy and security are still quite critical issues for the current IoV system because of various sensitive information and the centralized interaction architecture. To address these challenges, a decentralized architecture is proposed to develop a blockchain-supported IoV (BS-IoV) system. In the BS-IoV system, the Roadside Units (RSUs) are redesigned for Mobile Edge Computing (MEC). Except for information collection and communication, the RSUs also need to audit the data uploaded by vehicles, packing data as block transactions to guarantee high-quality data sharing. However, since block generating is critical resource-consuming, the distributed database will cost high computing power. Additionally, due to the dynamical variation environment of traffic system, the computing resource is quite difficult to be allocated. In this paper, to solve the above problems, we propose a Deep Reinforcement Learning (DRL) based algorithm for resource optimization in the BS-IoV system. Specifically, to maximize the satisfaction of the system and users, we formulate a resource optimization problem and exploit the DRL-based algorithm to determine the allocation scheme. The evaluation of the proposed learning scheme is performed in the SUMO with Flow, which is a professional simulation tool for traffic simulation with reinforcement learning functions interfaces. Evaluation results have demonstrated good effectiveness of the proposed scheme.

Due to intelligent electronic management, the smart grid has recently played a significant role in modern energy infrastructure. However, along with widespread deployment of the smart grid, many potential security threats (e.g., impersonation attack, replay attack, man-in-the-middle attack) rise to the surface. To defend against these possible attacks, numerous cryptography-based authentication schemes have been proposed for the smart grid. Most of the schemes investigate the secret key distribution problem, but the requirement of decentralized registration authority is neglected. In addition, over-complicated cryptographic primitives also strengthen the burden of authentication system. In contrast with previous researches, our proposed BlockSLAP utilizes cutting-edge blockchain technology as well as smart contract to decentralize the registration authority and reduce the interaction process to 2 steps. Moreover, our protocol is proved secure under computational hard assumption and informal security analysis. Finally, experimental results show that smart grid authentication performance in our protocol has been improved compared to the other existing ECC-related schemes.

Fault detection is a fundamental requirement for Industrial Internet of Things (IIoT), such as the process industry. This article first reviews the recent studies focusing on applying the fault detection techniques to the IIoT networks. However, we find that numerous studies focus on the resource utilization and workload allocation. The fault detection toward IIoT facilities is still in its immature stage because the existing approaches are not accurate enough for the stringent fault detection in IIoT networks. To this end, we present a novel algorithm, named Gaussian Bernoulli restricted Boltzmann machines (GBRBMs)-based deep neural network (DNN), to transform the fault detection into a classification problem. The real trace-driven experiments show that the proposed scheme outperforms other baseline machine learning methods. We anticipate that this article can inspire blooming studies on the related topics of smart IIoT networks.

Device-free localization (DFL), as an emerging technology that locates targets without any attached devices via wireless sensor networks, has spawned extensive applications in the Internet of Things (IoT) field. For DFL, a key problem is how to extract significant features to characterize raw signals with different patterns associated with different locations. To address this problem, in this paper, the DFL problem is formulated as an image classification problem. Moreover, we design a three-layer convolutional autoencoder (CAE) neural network to perform unsupervised feature extraction from raw signals followed by supervised fine-tuning for classification. The CAE combines the advantages of a convolutional neural network (CNN) and a deep autoencoder (AE) in the feature learning and signals reconstruction, which is expected to achieve good performance for DFL. The experimental results show that the proposed approach can achieve a high localization accuracy rate of 100% for a reasonable grid size on the raw real-world data, i.e., the collected raw data without added Gaussian noise, and is robust to noisy data with a signal-to-noise ratio greater than -5 dB. Additionally, its time cost for the classification of a single activity is 4 ms, which is fast enough for the IoT applications. The proposed approach outperforms the deep CNN and AE in terms of localization accuracy and robust ability against noise.

As an emerging technology, device-free localization (DFL), using radio frequency (RF) sensor networks to detect targets who do not carry any attached devices, has spawned extensive applications. Many existing works formulate DFL as a classification problem, and a key problem is how to extract discriminative features to characterize the raw wireless signal. In this paper, we present an autoencoder-based deep neural network for feature extraction, moreover, multiple Gaussian Bernoulli restricted Boltzmann machines (GBRBMs) are utilized for pre-training and dimension reduction. Experiment results show that this method of GBRBM-based autoencoder (GBRBM-AE) can achieve a high accuracy and efficient performance, which outperforms the conventional autoencoder. When the dimensions of input data are reduced from 784 to 20 dims, our algorithm can maintain a high accuracy of 97.1% and is robust to noise with SNR = 5dB.