ABSTRACT The industrial Internet of things (IIoT) integrates IoT technologies into industrial environments, enhancing automation, monitoring, and manufacturing efficiency. However, interconnected devices and sensors introduce significant security and privacy challenges, making intrusion detection a critical requirement. This study proposes a comprehensive progressive graph convolutional networks approach for enhancing the security of IIoT through advanced intrusion detection systems for threat identification and mitigation (PGCN‐IoT‐AIDS‐TIM). Data were collected from the ToN_IoT dataset and preprocessed using a regularized bias‐aware ensemble Kalman filter (RBAEKF) to clean, normalize, and balance the data. Relevant features were selected using the multiplayer battle game‐inspired optimizer (MBGIO). The selected features were then classified using progressive graph convolutional networks (PGCN) to identify normal and attack traffic. To improve detection accuracy, the Bitterling Fish Optimization Algorithm (BFOA) was employed to optimize PGCN parameters. The proposed framework was implemented in Python and evaluated using accuracy, precision, recall, F ‐measure, specificity, ROC, error rate, and computational time. Results demonstrated superior performance compared with CNN‐ID‐IoT, DNN‐AI‐ID‐IoT, and LSTM‐IDA‐IoT methods, achieving substantial improvements in accuracy, precision, and recall.
ABSTRACT Rapid growth in the IoV domain has led to significant security risks, making it imperative to use state‐of‐the‐art intrusion detection systems. Conventional methods usually lack the ability to effectively learn spatial, temporal, and global properties from the vehicular traffic data streams. Therefore, this study introduces a novel IDS named FusionIDS. It is different from previous convolutional neural network long short‐term memory (CNN‐LSTM) approaches in that this study considers the integration of Transformer‐based global attention along with SHAP explanations that are specific to IoV IDS. FusionIDS consists of three components, namely, a 1‐dimensional convolutional neural network (1D‐CNN) for extracting local features, an LSTM network for learning sequential information, and a Transformer encoder for global information extraction. In addition, it uses SMOTE to manage the problem of severe class imbalance and SHapley Additive exPlanations (SHAP) for explainability. Results show that FusionIDS obtains around 98% accuracy when tested on various attack cases in IoV environments.
ABSTRACT The decentralized nature of Web 3.0 forms networks of autonomous, heterogeneous entities, including smart contracts and dapp modules, among others, which operate on untrusted infrastructure for interactive communication. In such environments, component churn is frequent, link conditions vary in real time, and the logical topology is perpetually in flux. Traditional routing paradigms that depend on static views or centralized controllers are ill‐suited for this setting, as they incur high overhead, exhibit poor adaptability, and introduce single points of failure. Meanwhile, ensuring end‐to‐end data authenticity and processing integrity across multiple relay hops remains a critical yet unresolved concern in the absence of trusted intermediaries. To tackle these intertwined challenges, we propose DIP‐BLS, a unified protocol that couples dynamic interaction planning with BLS aggregate signatures into a coherent data exchange framework. On the one hand, each node independently maintains a local interaction plan that tracks neighbor connectivity, latency, available bandwidth, and load through lightweight probes, enabling multi‐metric forwarding decisions without global coordination. On the other hand, every relay hop appends a BLS signature to the packet, and these signatures are progressively aggregated into a constant‐size credential, permitting efficient batch verification at the destination. Theoretical analysis and simulation experiments demonstrate that, compared with shortest‐path and flooding‐based methods, DIP‐BLS achieves marked improvements in data delivery ratio, average interaction latency, and system throughput, while providing strong tamper resistance and identity traceability at acceptable overhead—well‐suited to Web 3.0 distributed application scenarios that demand both high efficiency and robust security.
ABSTRACT Underground surveying and mineral exploration increasingly rely on high‐resolution panoramic imaging for digital‐twin construction and remote operation. Yet transmitting massive visual data from remote mining sites is constrained by limited terrestrial infrastructure, high latency, and strict privacy requirements for sensitive geological assets. This paper proposes a Low‐Latency Privacy‐Preserved Data Sharing Framework (LPP‐DSF) for Space‐Air‐Ground Integrated Networks (SAGINs). We design a hierarchical architecture where UAVs act as mobile edge computing (MEC) relays to provide reliable air–ground connectivity and edge preprocessing for underground gateways. We formulate a joint offloading and resource allocation problem that explicitly includes the UAV‐to‐satellite backhaul delay, so as to minimize end‐to‐end latency and gateway‐side energy under link‐reliability and UAV capacity constraints, and develop a priority‐aware greedy offloading algorithm for real‐time decisions. To ensure sovereignty and auditability, LPP‐DSF integrates CP‐ABE with a consortium blockchain for fine‐grained access control and tamper‐evident provenance. Simulations show up to 25% lower latency than representative greedy baselines and about 65% lower latency than direct satellite transmission, while maintaining high throughput and negligible security overhead.
ABSTRACT Digital documents have been widely used on several domains and stored on digital platforms so that documents can be accessed from anywhere but unauthorized access to the digital documents may lead to fraudulent activities. Various frameworks integrated with advanced technology and cryptographic techniques are proposed to enhance the security of digital documents. This paper proposes Blockchain Technology and Quantum Cryptography integrated framework Quantchaindoc to enhance the security of digital documents and ease availability for the document verification process. Blockchain Technology improves security, providing a transparent, traceable and secure environment while maintaining users' privacy and Quantum Cryptography prevents quantum as well as cyberattacks. The proposed Quantchaindoc framework stores digital documents on IPFS and the hash value of digital documents generated by IPFS will be further encrypted using Advanced Encryption Standard‐256 (AES‐256) encryption. For encrypting hash value, AES‐256 uses the key generated by BB84 protocol, a quantum cryptographic protocol. The resultant encrypted hash value is stored on the Blockchain network which is accessed by the owner or Admin only. The framework assures access control of digital documents to owners only and allows authenticated organizations to access digital documents on demand. The proposed framework has been analyzed using evaluation metrics, that is, Throughput (TPS), Gas Consumption, Encryption Overhead, Latency, Quantum Attack Analysis and Energy Consumption. Results show that the proposed work improves the security of digital documents, prevents unauthorized access, and protects against cyberattacks and quantum attacks.
