The industrial control anomaly detection algorithm effectively monitors and identifies anomalies in industrial control systems, ensuring timely issue detection to maintain system reliability, safety, and regular operation. Researchers often integrate a large number of high-precision, high-sampling-frequency sensors into the system to obtain comprehensive real-time equipment operating data. However, deploying consumer-grade high-performance computing devices in large-scale industrial control systems is often impractical, rendering existing anomaly detection systems unusable in practical environments. Most existing sensor feature selection algorithms are unsuitable for industrial control time series datasets, and some suffer from issues such as a limited reduction in the number of sensors and a decrease in anomaly detection accuracy after reduction. We propose an offline industrial control anomaly detection algorithm based on the Maximum Information Coefficient (MICHC-NN) to address these challenges. We utilize the Maximum Information Coefficient to compute correlations among sensors, addressing the issue of existing feature selection algorithms relying on anomaly labels. Additionally, we introduce a key sensor selection algorithm based on correlation, identifying interrelated sensors in an industrial control system and determining the key sensors most susceptible to system anomalies. These key sensors allow the anomaly detection algorithm to be deployed with a smaller footprint in real industrial control environments. Finally, we employ an autoencoder-decoder model with attention mechanisms to train the anomaly detection network. Experimental results demonstrate that our proposed anomaly detection algorithm achieves performance comparable to using all sensors, with a 75% reduction in the number of sensors employed.

Secure authentication and key agreement across heterogeneous trust domains is essential for reliable collaboration in the Industrial Internet. However, existing cross-domain AKA schemes still face certificate-management overhead, key-escrow risk, insufficient identity privacy, and high computational cost. To address these issues, this paper proposes LBCAK, a lightweight blockchain-assisted anonymous cross-domain AKA scheme. LBCAK combines a certificateless-style key construction with AuthLedger-based public-state coordination, where the blockchain maintains current registration states for cross-domain credential validation while online authentication and session-key establishment remain off-chain. Credential-state-specific protected identifiers are further used to reduce on-chain linkability without exposing raw identities or private credential materials. Security is analyzed under an eCK-style leakage model in the random-oracle setting and further examined using ProVerif, showing that LBCAK provides mutual authentication, identity privacy, untraceability, forward secrecy, and resistance to major active and key-leakage attacks. Performance evaluation shows that LBCAK reduces computational and communication overheads by 14.3% and 4.2%, respectively, demonstrating its practicality for resource-constrained Industrial Internet deployments.

Blockchain has been increasingly adopted in the Industrial Internet of Things (IIoT) for cross domain authentication, enabling trusted collaboration among multiple management domains and supporting decentralized trust management. However, existing authentication schemes, particularly those based on classical public-key cryptography such as RSA and ECC, are vulnerable to quantum attacks and still face significant challenges in computation overhead. These limitations make them insufficient for the practical demands of complex, multi-domain environments, especially in the context of future quantum threats. To address these challenges, this article proposes PQEdgeAuth, an edge-assisted authentication scheme for IIoT that integrates post-quantum secure authentication with an optimized consensus protocol. The scheme incorporates post-quantum cryptographic techniques to ensure long-term security against quantum adversaries, while employing location-based grouping and aggregation signatures to reduce consensus communication overhead and enhance scalability. Experimental results show that PQEdgeAuth reduces computation time by up to 35.23% and improves throughput by up to 54.38% compared to existing schemes, demonstrating its potential to offer a secure, scalable, and efficient solution for cross domain authentication in IIoT environments.

CSI-based physical-layer authentication (PLA) is a promising solution for securing access in wireless communication systems. However, distribution shifts caused by time-varying CSI and computational limitations due to constrained edge resources jointly degrade authentication accuracy. Existing methods lack effective modeling of the temporal dynamics and long-range dependencies in time-varying CSI and high computational costs, resulting in limited robustness and generalization in dynamic environments. To address these challenges, we propose LRTV-PLA, a lightweight and robust PLA with time-varying distribution shift in dynamic wireless environments. Specifically, we propose temporal interpolation with contrastive regularization (TICR) to address sparse CSI sequences by interpolating missing data and constraining cross-time variation. We also adopt a shallow autoencoder for nonlinear dimensionality reduction (SAE-NLDR) to reduce cost while preserving discriminative features. We further introduce LinA-BiLSTM, which combines bidirectional LSTM for short-term dependencies with linformer-based mulit-head attention for efficient long-range modeling, reducing attention complexity from$O(n^{2})$to$O(n)$and enabling lower latency and memory usage on edge devices. Finally, extensive experiments conducted in real-world scenarios demonstrate that the proposed method outperforms existing approaches in accuracy, robustness, and resource efficiency. For example, in the street scenario, our method improves authentication accuracy by 1.47% compared to the best baseline, shortens authentication time by approximately 60%,decreases memory usage by around 36%, and reduces the standard deviation from 1.52 to 0.95.

With the rapid advancement of industrial structures, many factories are deploying anomaly detection systems. However, most existing anomaly detection algorithms are unsuitable for high-noise industrial control system (ICS) environments and constantly changing sensor patterns, resulting in low detection accuracy. To address these challenges, we propose a hierarchical temporal memory-based online anomaly detection algorithm for ICS, incorporating cumulative distribution functions and Gaussian convolution kernels. We enhanced the encoding process of hierarchical temporal memory, enabling it to quickly fit the physical processes of ICS and remember the system’s previous operating states for anomaly recognition. Additionally, our anomaly calculation algorithm, based on cumulative distribution functions and Gaussian kernel convolution, effectively addresses the adaptability and accuracy challenges posed by high-noise ICS environments. Experimental results demonstrate that our method outperforms the best baseline algorithms. While maintaining good time stability, our method achieves a 9$\%$improvement in detection accuracy, highlighting its significant advantage in balancing detection precision and time performance.

The convenience of wireless communication has led to its widespread adoption across various application scenarios. However, existing Physical Layer Authentication (PLA) schemes still have limitations in terms of adaptability. This paper introduces a novel method based on key-less channel-based authentication framework designed to enhance PLA performance under different mobility patterns. Our method combines the sliding window with a model based on a siamese neural network, enabling a fully connected neural network classifier to effectively distinguish between legitimate transmitters and potential attackers, achieving an accuracy of 97.91%. Experiments conducted in various environments demonstrate the robustness and high performance of the proposed method, making it well-suited for deployment in time-varying environments with high-security demands and resource constraints.

The emergence of digital twin technology offers a promising solution to address the limitations of traditional methods on early diagnosis and accurate propagation analysis of flight ground service delays. However, the application of digital twin technology in the civil aviation domain still stays at the lower maturity of the L2 level, which focuses on physical assets, operational data, and maintenance planning at airports, and failed to achieve the integration of flight ground operation mechanism and real‐time data, making it difficult to realize timely delay diagnosis. The simulation model is also limited to the offline simulation technology, which cannot connect to real‐time data for simulation from intermediate processes. In this work, we developed an advanced L3‐level airport digital twin system for flight ground service processes delay diagnosis and propagation, which focused on real‐time data‐driven simulation models and machine learning applications to meet the timely and precision requirements. First, we used the Unity3D platform to construct static three‐dimensional models of flight ground service objects on the airport cloud server. By parsing these behavioral state interfaces and mapping real‐time dynamic data from the airport sensing and business systems, we achieved accurate visualization of the airport’s dynamic operational processes. Then, a vehicle delay tree–based Bayesian diagnostic model was proposed in the digital twin system to analyze the relationships between multiple flights and service processes, which enables proactive diagnosis of the operation status and provides delay warning information. To improve the accuracy of propagation analysis, we proposed a “breakpoint” simulation method that enables real‐time simulation starting from an intermediate moment, facilitating the inference of flight ground service delays since the early warning moment. In addition, two delay tracing and propagation algorithms were proposed to identify delays and investigate propagation paths. Leveraging real‐time operational information, our approach provides valuable feedback for decision‐making, empowering the airport manager to formulate precise optimization strategies. Experiments on real‐world airport data have validated the effectiveness of our proposed method and provided practical recommendations for airport managers to reduce aircraft delays and improve airport operation efficiency.

Reliable and robust airport flight ground service resources collaborative scheduling has emerged as an issue of major concern at congested airports, which has great significance on airport service quality and operation efficiency. Due to the uncertainty of the service process of multiple vehicles in airport ground operations, the combination of simulation and optimization (sim-opt) techniques has been proven to be an effective means to solve the collaborative scheduling problem. However, existing models still have limitations on the reliability of feasible solutions and the robustness of evaluations. In this work, we developed a conflict resolution based sim-opt framework that combines the search capability of mathematical optimization models with the ability of simulation models to describe uncertainty. The dynamic window is introduced to divide the flight zone into multiple sub-intervals, which allows for iterative search and evaluation of the optimal schedule solution while reducing model calculation time. Additionally, a matching algorithm based on slack and tight constraints is proposed to optimize the possible resource requirements of each vehicle, thereby improving the initial schedule solution’s search depth and reliability. Furthermore, a conflict resolution feedback mechanism between adjacent windows is constructed to optimize the scheduling plan, reducing misjudgment and omission of the optimal solution and enhancing the sim-opt framework’s robustness. Finally, experiments on real airport datasets of three different scales demonstrate that our proposed method efficiently allocates resources with a smaller flight delay time as well as reducing total vehicle cost time during the flight ground service process.

Industrial control systems rely on wireless devices and sensors, necessitating critical security. Physical layer authentication (PLA) is a promising mechanism for device authentication, utilizing its unique spatiotemporal characteristics and channel state randomness, which offers unforgeability and high informatics security with low computational overhead and efficiency in resource-constrained scenarios. However, existing PLA mechanisms face challenges in complex industrial wireless environments, including insufficient accuracy, computational complexity, inadequate noise consideration, and poor performance. To address these challenges, we propose a convolutional denoising autoencoder (CDAE) model that reduces feature dimensions, eliminates noise, and extracts key vectors. The weighted$k$-nearest neighbor algorithm classifies the extracted vectors for comprehensive authentication in control system networks. Accurate authentication enables efficient detection of malicious attacks. Simulation experiments show that using CDAE-extracted feature vectors achieves over 95% accuracy with only 1% training samples, surpassing channel state information-based authentication by 46.15%, validating the proposed mechanism’s effectiveness.

Recently, the use of wireless channel state information (CSI) to generate encryption keys in the physical layer has gained significant attention from researchers. Unlike classical cryptography, this approach relies on the variability of the wireless channel, channel reciprocity, and spatial decorrelation to ensure security, making it more lightweight and providing strong randomness. This article proposes a physical layer key generation scheme for wireless LAN MIMO systems based on feature fusion autoencoder (FFAEncoder) to address the issue of a high key disagreement rate (KDR). Our approach involves extracting amplitude and phase features separately, fusing them through multiplication operator in a neural network, and using an autoencoder to extract common features. The proposed scheme was evaluated on multiple data sets in different real-world scenarios, and it was found that the transmitter and receiver codeword’s mean squared error (MSE) and mean absolute error (MAE) were smaller than those of the current models, indicating better key generation performance. Additionally, the proposed scheme’s KDR was smaller, with a decay rate faster than that of the other two models in the same environment, and the primary key bits were 1/2 and 1/3 that of the other models, respectively, as the signal-to-noise ratio (SNR) increased.

With the development of big data, data sharing has become a hot topic. According to the previous research on data sharing, there is a problem with regard to how to design an effective incentive mechanism to make users willing to share data. First, we integrate the incentives based on reputation and payment and introduce “credibility coins” as a cryptocurrency for data-sharing transactions, to encourage users to participate honestly in the data-sharing process based on federated learning. Second, we propose a dynamic incentive model based on the evolutionary game theory to model the game process of users in data sharing and analyze the stability of their strategies. Finally, based on the results of this analysis, we use the blockchain-based smart contract technology to dynamically adjust the participation benefits of users under different conditions in order to promote users to join consortium blockchains more often and steadily to participate in model training for federated learning and obtain better model accuracy. Our work is the first to apply the evolutionary game theory to the study of incentives in federated learning, and plays a leading role in the study of incentives in federated learning. Experimental simulation validation shows that our DIM-DS model can adequately motivate users to participate in the collaborative task of data sharing and maintain stability. The model can maximize the effectiveness of the federated learning model.

Neighbor discovery is a prerequisite procedure in communication among energy-limited Internet-of-Things (IoT) devices. Discovering neighbors should be achieved in an energy-efficient way. Communication between heterogeneous IoT devices is a common phenomenon for achieving extensive connections among IoT devices. Generally, existing asymmetric neighbor discovery methods are evolved from the symmetric methods and lack specialized designs for heterogeneous IoT devices which led to relatively poor energy-efficient performance. In this article, we use a generalized slot model using pure sending (PS) slot and pure listening (PL) slot, called PSPL, to convert existing neighbor discovery algorithms (symmetric and asymmetric) to highly energy-efficient asymmetric ones. The core idea of PSPL is that to achieve highly energy-efficient one-way discovery, PS slot and PL slot are only used in devices with smaller and larger energy budgets, respectively, and the length of PL slot is much larger than that of PS slot. In this way, the energy efficiency of asymmetric neighbor discovery can be substantially improved. After implementing one-way discovery, two-way discovery can be easily achieved with mutual assistance. Two examples of how to convert existing algorithms into highly efficient asymmetric ones are presented, the one is to convert Disco to Disco+PSPL and the other one is to convert Griassdi to Griassdi+PSPL. The theoretical analysis results indicate that with the same energy budget, Disco+PSPL and Griassdi+PSPL reduce the worst case latency bounds by up to 82.28% and 87.69%, respectively, compared with the best known asymmetric solutions. The simulation evaluation verifies the effectiveness of our designs.