ABSTRACT The healthcare business relies heavily on Internet of Things (IoT) devices to automatically gather health parameters like temperature, heart rate, blood pressure, and so on. However, security, privacy, and dependability concerns are the largest obstacles facing IoT devices in the healthcare sector. The Blockchain Hyperledger Fabric (HLF) framework's characteristics reduce transaction costs, offer high security, and guard against attacks. So, the proposed work utilized the HLF with a novel Aspect Authority: Access Permission Schema (AAAPS) consensus algorithm for improving the security of healthcare data. The healthcare IoT sensors estimate the patient's heart rate, temperature, blood pressure, etc. Initially, the IoT and HLF are integrated through the Message Queuing Telemetry Transport (MQTT) protocol and Node‐RED tool. The collected IoT data is stored in the HLF blockchain by following specific procedures, including authenticating the user through the JSON Web Token (JWT) and Certificate Authority (CA). Then, the transaction is evaluated through the endorsing policy, and the consensus is calculated using the AAAPS algorithm. After getting consensus from other nodes presented in the HLF, the IoT data is stored in the ledger storage. The web‐based application is created for this work, and it is evaluated using the latency, response time, etc. The application attained high throughput and less response time. The highly secure remote monitoring healthcare application is made possible by integrating the HLF with a novel consensus algorithm into IoT devices.

ABSTRACT Keyword search over encrypted information allows workers to quickly locate the most relevant outcomes; cloud computing researchers have researched keyword search over encrypted data extensively. The effective ciphertext search and dynamic updates by forward security cannot be achieved at the same time by currently available ranked multikeyword search systems. In this manuscript, an Optimized Nonlocal Kernel Network‐Based Keyword Search for Comprehensive Detection and Prevention of Forward Security Threats in Encrypted Cloud Data Systems (ONKN‐KS‐FST‐ECD) is proposed. The ONKN‐KS‐FST‐ECD model uses the Nonlocal Kernel Network (NKN) approach to achieve forward security by reducing search complexity while maintaining search accuracy and by preventing cloud servers from using earlier tokens to make search queries over newly uploaded files. The Circulatory System‐Based Optimization Algorithm (CSBOA) is used to improve the weight parameter of the NKN model to increase the search accuracy. The proposed ONKN‐KS‐FST‐ECD method attains 24.28%, 28.22%, and 29.27% higher search accuracy and lower file updating time of range 14.76%, 16.82%, and 12.47% compared to existing techniques such as ranked keyword search over encrypted cloud data over machine learning approach (RKS‐ECD‐ML), a system for privacy‐preserving and effectiveness search over encrypted social graphs (PPKSS‐ESD‐CC), and multiclient secure and efficiency dpf‐dependent keyword search for cloud storage (DDPS‐SCDSC) respectively.

ABSTRACT As libraries adapt to the digital age and the challenges posed by the evolving information landscape, adopting emerging technologies becomes paramount. With its reputation for security, transparency, and decentralization, blockchain technology has come to light as a potentially revolutionary instrument that might completely change library services and operations. This review paper explores how blockchain has been applied and adopted to suit libraries, shedding light on its transformative capabilities and its benefits. It begins with an introduction to blockchain technology, characterized by critical features and historical context. It then delves into specific use cases within libraries, ranging from cataloguing and metadata management to digital asset preservation and copyright management. Real‐world case studies and examples are presented to illustrate the practical implementation of blockchain in libraries and their consortia. While blockchain technology holds great potential, this article also identifies the implementation obstacles that libraries may encounter, such as budgetary constraints, issues with scalability, and compliance with regulations. Ethical and privacy considerations are examined, emphasizing the need to protect patron data and ensure responsible usage of blockchain technology in a library environment. Additionally, this research review outlines the many advantages and possibilities that blockchain offers libraries, including increased user trust, decreased fraud, and flexibility to accommodate changing user requirements.

ABSTRACT Video surveillance is the main requirement of smart urban development and government security, and the traditional systems need live monitoring with manual supervision of the information; hence, there is a limitation on the practical use of the data gathered through surveillance. In the study, the deep fusion network architecture is considered the main architecture to identify objects in video materials, followed by enhancing them for real‐time security systems. The system employs you only look once v9 (YOLOv9) to detect objects precisely, thus allowing fast tracking and identification of suspicious items. The framework uses adaptive motion analysis to choose significant frames for efficient analysis purposes and automatic event monitoring procedures. Semantic efficient sub‐pixel convolutional neural networks (ESPCN) perform high‐resolution enhancement using low computational resources, which feature pyramid networks (FPN) systems use to extract multi‐scale features for better image clarity during super‐resolution processing. The Poisson Image Editing method allows users to combine. To maintain visual coherence while incorporating the enhanced items into video footage, use File Writer. This paper introduces a novel approach that combines segmentation models, detection models, resolution enhancement tools, and a post‐processing function. This approach enhanced real‐time video surveillance with improved accuracy and processing speed. The experimental data confirms the system works effectively in real‐life surveillance through a 90.5% accuracy rate achieved alongside 50 predictions per second processing abilities.

ABSTRACT The quick growth of the Internet of Vehicles (IoV) needs secure, low‐latency, and trusted communication frameworks. Existing Intrusion Detection Systems (IDS) are struggling to provide effective threat detection in changing vehicular networks. This creates a critical need for adaptive detection mechanisms that can perform efficiently at the network edge. According to this background, we propose a Zero‐Tuned Peripheral Stacked Ensemble (ZTPSE) for efficient attack detection and mitigation in practical VANET environments. In this framework, lightweight base learners run on edge nodes to detect attacks locally. A central meta‐learner then combines these local outputs using stacking without manual tuning. This design allows fast and efficient detection under different traffic densities and attack scenarios. By combining distributed detection with ensemble learning, ZTPSE detects multiple attack types and allows the system to take a rapid response action. Simulation results indicate that the proposed ZTPSE framework achieves high detection accuracy of 95.70% in varying traffic densities and maintains a low false‐positive rate (FPR) of 0.05%.

ABSTRACT Integrating intelligent reflective surfaces (IRSs) with millimeter‐wave (mmWave) massive MIMO systems is a promising strategy for enhancing the performance of next‐generation wireless networks. However, practical limitations complicate system throughput optimization, such as the restricted allocation of dedicated radio frequency (RF) chains per antenna and the passive nature of IRS phase shift elements. This paper demonstrates that the equivalent channel gain can be maximized by appropriately designing the IRS component phases to optimize spectral efficiency, resulting in challenging non‐convex optimization problems. To address this, we employ two approaches for maximizing channel gain. The first approach is an algorithm based on Eigenvalue Decomposition (EVD). Given the computational intensity required to calculate eigenvalues and eigenvectors, we propose an alternative algorithm based on gradient projection methods (GPM), which offers significantly reduced computational complexity despite a slight performance reduction compared to EVD. Additionally, we introduce a Phase Coherence Method (PCM) framework for designing hybrid precoder and combiner matrices in both the transmitter and receiver, addressing the challenge of uni‐modular matrices. Comprehensive simulations reveal that the proposed algorithms achieve near‐optimal performance, surpass alternative algorithms, and effectively advance the integration of IRS with mmWave massive MIMO systems.

ABSTRACT In Industrial IoT ecosystems, resource‐constrained devices increasingly rely on searchable encryption (SE) to offload encrypted data to cloud platforms, reducing local computational burdens while preserving data privacy. However, traditional SE technologies struggle to meet the demands of IoT environments featuring large‐scale distributed devices, due to cumbersome certificate management and inherent key escrow weaknesses. While certificateless searchable encryption (CLSE) schemes have been proposed to overcome these challenges, most existing solutions remain prone to security flaws and lack support for multi‐keyword searches. To address these shortcomings, we present a novel lightweight certificateless public key encryption scheme with multi‐keyword search capabilities (CLPSE). Crucially, compared to prior work, our protocol design does not rely on bilinear pairings, but instead employs an elliptic curve algorithm involving only scalar multiplication and addition operations. In addition, to protect the privacy between keywords and ciphertexts, we design a new matrix index structure using the BM25 retrieval algorithm and VFE function. Users can retrieve the corresponding ciphertexts using their trapdoor and threshold, which improves the flexibility of retrieval. Based on this construction, our scheme (CLPSE) is tailored for devices with limited storage and computational capacity. Compared to existing methods, it guarantees the indistinguishability of keyword ciphertexts and trapdoors, while delivering robust protection against both inside and outside keyword guessing attacks (IKGA and OKGA), thereby enhancing keyword security. Experimental results substantiate that our scheme achieves significant improvements in computational and communication efficiency over prior approaches.

ABSTRACT Modern aircraft are advancing toward intelligent development, but this connectivity exposes them to new cyber‐security threats. Most existing intrusion detection methods are designed for closed‐set scenarios and often perform poorly in open‐set environments with unknown attacks. We propose a novel open‐set intrusion detection system with an undetermined attack detector and a predefined attack classifier. In the first stage, a conditional Gaussian discriminative model is trained using known information and the added conditional Gaussian distribution. Reconstruction error distribution helps distinguish between known and unknown attacks. In the second stage, a gated recurrent unit network integrated with a temporal pattern attention mechanism is used to extract time‐series features from the airborne network data. By applying multi‐scale convolution operations to the hidden states of the Gated Recurrent Unit, the model effectively captures temporal dependencies and dynamic patterns within network traffic. The proposed method demonstrated promising detection results on MIL‐STD‐1553 and CICIDS2017, with experimental findings showing that it can detect both known and unknown attacks, thus serving as a viable solution for securing airborne networks.