The swift, secure transmission of data, especially sensitive data, such as that acquired from high-resolution image sensors, is a major focus of the Industrial Internet of Things (IIoT). Current strategies do not strike a balance between security and efficiency, causing messages to be lost and/or processing to be delayed to an unacceptable level. We present a new quantum-inspired quantum walk (Q-IQW) encryption protocol. The Q-IQW protocol is used to implement the first blockchain for the safe transfer of data between IIoT devices. For the first time, quantum hash functions based on Q-IQW are used to link blocks in a chain instead of traditional hash functions. The main contributions of the protocol are the efficient data transfer between IIoT nodes and the ability to fully control their data. This work focuses on simulating the protocol to understand the theoretical and empirical performance of the protocol. The results show that in terms of entropy of 7.99 and unified average changing intensity (UACI) of 33.56%, the protocol is very robust as evidenced by 99.58% number of pixel change rate (NPCR), and low correlation, i.e., the protocol exhibits very high security. Further, the protocol is reliable in that it can perfectly recover images under high distortion, for example, a 50% data occlusion in the encrypted images. These outcomes underscore the dependability, utility, and efficiency of the protocol in protecting IIoT information to make it a viable remedy for maintaining the integrity of the information during transfer and storage.

The proliferation of IoT devices has expanded the attack surface, making zero-day threat detection an urgent priority. This paper proposes LLMEdgeSec, a framework that leverages large language models (LLMs) for log reasoning to detect and contextualize zero-day exploits in real-time. Unlike conventional anomaly detectors that rely on static signatures or pre-defined rules, LLMEdgeSec applies prompt-based reasoning over system logs, communication metadata, and device telemetry, enabling proactive identification of previously unseen attack vectors. The framework employs a dual-stream architecture: (1) a lightweight transformer encoder on edge nodes for local feature extraction, and (2) a cloud-based LLM interface for contextual reasoning and cross-device threat correlation. A zero-shot calibration method mitigates hallucinations and reduces false positives. Evaluation on TON IoT, CIC-IDS2018, and a real-world smart factory dataset demonstrates F1 scores exceeding 92% on zero-day samples with communication overhead below 8MB per federated round. Results show significant improvements in accuracy, interpretability, and adaptability over state-of-the-art baselines.

The rapid expansion of Internet of Medical Things (IoMT) networks has enabled continuous data collection from diverse medical sensors and devices, supporting real-time monitoring, diagnostics, and decision-making. However, the resource limitations of IoT nodes and the open nature of communication channels make healthcare data vulnerable to security and privacy breaches. To address these challenges, this paper presents a blockchain-assisted, privacy-preserving signature scheme leveraging Quantum Key Distribution $(\mathcal {QKD})$ to ensure secure and trustworthy data sharing in Healthcare Internet of Things (H-IoT) environments. The proposed scheme integrates a quantum-designated verifier signature mechanism with a private blockchain infrastructure, where peer nodes validate and store healthcare data securely. Formal (software-based) and informal security analyses demonstrate the scheme's resistance to forgery, replay, and quantum attacks. Simulation experiments conducted in Python show that the proposed protocol achieves strong cryptographic performance, with a computational cost of 42.1ms and a communication overhead of 834 bits. Additionally, a blockchain-based prototype implementation quantifies the time required to append various numbers of blocks and process multiple healthcare transactions, confirming the scalability and practicality of the proposed solution. The results affirm that the scheme offers a reliable, efficient, and quantum-resilient framework for securing sensitive medical data across distributed IoMT healthcare systems.

Convolutional neural networks are widely used in various segmentation tasks in medical images. However, they are challenged to learn global features adaptively due to the inherent locality of convolutional operations. In contrast, MLP Mixers are proposed as a backbone to learn global information across channels with low complexity. However, they cannot capture spatial features efficiently. Additionally, they lack effective mechanisms to fuse and mix features adaptively. To tackle these limitations, we propose a novel Dynamic Decomposed Mixer module. It is designed to employ novel Mixers to extract features and aggregate information across different spatial locations and channels. Additionally, it uses novel dynamic mixing mechanisms to model inter-dependencies between channel and spatial feature representations and to fuse them adaptively. Subsequently, we incorporate it into a U-shaped Transformer-based architecture to generate a novel network, termed the Dynamic Decomposed MLP Mixer. We evaluated it for medical image segmentation on two datasets, and it achieved superior segmentation performance than other state-of-the-art methods.

The integration of artificial intelligence (AI) with the Internet of Things (IoT) has transformed numerous domains through the AI of Things (AIoT). Nonetheless, AIoT encounters issues related to energy usage and carbon emissions as mobile technology continues to progress. Generative AI (GAI) possesses significant potential to mitigate carbon emissions associated with AIoT, owing to its higher reasoning and generative powers. Conventional security protocols frequently encounter issues with computational efficiency, latency, and overall security comprehensiveness. Blockchain technology, characterized by its decentralized and immutable properties, is a viable approach for improving electronic healthcare data transmission and node authentication in IoT networks. This research examines secure data transmission and node encryption in IoT systems, with a particular emphasis on data management. Conventional approaches encounter constraints in computational efficiency, latency, and comprehensive security. This study presents a novel protocol that combines GAI and blockchain technology with quantum encryption to enhance authentication and ensure secure data transmission. The algorithm comprises multiple consecutive processes, including the encoding and transmission of node requests, followed by the authentication process utilizing hash functions and digital signatures. The authentication approach utilizes a challenge-response technique, guaranteeing that only nodes with authentic credentials can advance. Thereafter, a dynamic key exchange protocol and quantum encryption method provide secure data delivery. The results indicate the procedure’s effectiveness in ensuring secure and regulated access to patient data, underscoring its significance in medical facilities. The system’s functionalities are augmented by a thorough evaluation employing machine learning. The findings indicate that the system exhibits an accuracy of 99.4%, precision of 99.10%, recall of 98.66%, F1-score of 98.50%, and security of 99.2%. An extensive analysis and comparison with the state-of-the-art methods demonstrate the significant advancements of the suggested method in tackling cryptographic security challenges. The algorithm offers a thorough approach to protecting IoT applications, especially in managing healthcare data.

The emergence of 6G networks enhances the speed and compatibility of Internet-of-Things (IoT) devices in vehicular ad hoc networks (VANETs), leveraging underutilized bands to improve wireless communication and security, though its adaptability may introduce cyber vulnerabilities; to address this, we propose an energy-efficient consortium-based blockchain-enabled heterogeneous (EBH) 6G network for IoT devices, offering secure VANET control through a lattice-based ring signcryption scheme that ensures timely message relaying while preserving vehicle anonymity and cloud data confidentiality, with blockchain blocks formed via secure peer nodes and service provider data; our protocol’s security was rigorously validated through analysis and Python-based implementation, achieving 42.1 ms computational cost and 1026-bit communication overhead, and proving effectiveness across varying block and transaction loads, while guaranteeing key security properties-anonymity, linkable privacy, unforgeability, and confidentiality-even under quantum threats, using lattice-based cryptography, Zero-Knowledge Proofs (ZKP), and blockchain immutability.

Digital twins-enabled transportation cyber-physical systems are employed in the transportation sector to enhance environmental quality, mobility, and safety. They are digital representations of transportation networks, enable the simulation of these networks’ behavior under various conditions. They can be employed in various transportation sectors, including forecasting traffic congestion, facilitate the optimization of flow and safety, enhance the efficiency of public transportation, improve the efficiency of freight transportation by offering support in this process, facilitates the consideration of future multimodal infrastructure development requirements, furnish drivers with up-to-date information about weather alerts, road closures, and traffic situations in real time, assess numerous aspects like, impacts of various mobility operators, transport users, and environmental conditions. However, the real-time data synchronization in digital twins-enabled transportation cyber-physical systems is accomplished using an open communication channel. Regrettably, the utilization of virtual-reality synthesizing security threats in the network necessitates the implementation of stringent privacy and security procedures, including authentication, encryption, and signature approaches. This study proposes a quantum-key-distribution (QKD)-based authentication protocol for secure communication of digital twins-enabled transportation cyber-physical systems. The integrated quantum in the system ensures the compactness and verifiability of data. The protocol’s security is examined using the Scyhter tool and is verified to be secure by informal security analysis. The proposed scheme achieves its efficiency over current solutions. Moreover, the latest technology and techniques are used to examine the operational capabilities and security features.

Summary The convergence of reliable and self‐organizing characteristics of Wireless Sensor Networks (WSNs) and the IoT has increased the utilization of WSN in different scenarios such as healthcare, industrial units, battlefield monitoring and so forth, yet has also led to significant security risks in their deployment. So, several researchers are developing efficient authentication frameworks with various security and privacy characteristics for WSNs. Subsequently, we review and examine a recently proposed robust key management protocol for an industrial sensor network system. However, their work is incompetent to proffer expedient security and is susceptible to several security attacks. We demonstrate their vulnerabilities against man‐in‐the‐middle attacks, privileged insider attacks, secret key leakage attacks, user, gateway, and sensor node impersonation attacks, and offline password‐guessing attacks. We further highlight the design flaw of no session key agreement in Itoo et al. Therefore to alleviate the existing security issues, we devise an improved key agreement and mutual authentication framework. Our protocol outperforms Itoo et al.'s drawbacks, as demonstrated by the comprehensive security proof performed using the real‐or‐random (ROR) model and the formal verification accomplished using the Automated Validation of Internet Security Protocols (AVISPA) tool.

The Internet of Medical Things (IoMT) is a compelling networking paradigm integrating wireless communications sensors, connected devices, and embedded computing technologies. The IoMT involves the collection of real-time health data using sophisticated medical sensors. In recent years, the IoMT has become increasingly significant within the broader context of the Internet of Things (IoT). It provides accessibility for health monitoring and poses security obstacles to safeguarding the confidentiality and privacy of patient data. Therefore, this article presents a blockchain-integrated quantum authentication scheme in sensor-assisted IoMT networks. The proposed concept utilizes blockchain technology to achieve efficient patient authentication without the need for third-party entities. In addition, a secure quantum authentication scheme is designed not to require patients to authenticate themselves when communicating with multiple doctors simultaneously. This protocol explicitly addresses how clinicians can misuse their professional roles toward patients in IoMT networks. An evaluation analysis assesses the proposed technique’s efficacy compared to existing authentication schemes. The performance analyses demonstrate that the proposed protocol is resilient against various security attacks. Also, the practical usability of the quantum authentication scheme proved its importance as a significant improvement in communication security for IoMT networks.

In recent years, nanotechnology has emerged as a significant field of study with far-reaching implications. Integrating this nanoscale technology into the Internet of Things (IoT) has given rise to the Internet of Nano Things (IoNT) paradigm. This revolutionary technology significantly impacts healthcare, smart homes, and defense. This technological advancement has opened up new possibilities for enhancing the efficiency and effectiveness of these areas and has the potential to revolutionize their approach. Many research initiatives are addressing the definition of secure, scalable, and reliable network architectures at the nanoscale. However, the nano nature of this technology poses several security challenges for secure data transmission. Authentication is one of a prerequisite for secure data transmission. Our study presents a novel elliptic curve cryptography-based authentication protocol for IoNT in this context. Since the nanoscale devices have less computational capabilities, this protocol leverages a secure hash function and XOR operations to ensure lightweight yet robust authentication. The protocol seamlessly integrates molecular and electromagnetic communication methods, thereby enhancing both security and efficiency within IoNT networks. In such a hybrid approach, parameter settings and message exchange are properly devised to achieve the aforementioned security services effectively. The security and authentication aspects of the protocol are rigorously examined using the real-or-random (ROR) model and Burrows–Abadi–Needham (BAN) logic, with its resilience against security threats tested via the automatic validation of Internet security protocols and application tool. We also analyze communication costs, computational overhead, and real-world feasibility. Finally, by employing the Nano-sim and N3Sim tools, we simulate and demonstrate the Internet of Nano-Things environment (i.e., expressed in terms of the packet loss ratio and the average amount of time required for propagation of authentication messages through molecular communications). This research stands as a pivotal contribution, fortifying the foundations of IoNT through heightened security and enhanced reliability.