The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure. The capability to examine and inspect such large-scale information and perform analytical protection over the large volumes of personal and organizational information demands authentication and confidentiality so that the total data are not endangered after illegal access by hackers and other unauthorized persons. In parallel, these large volumes of confidential industrial data need to be processed within reasonable time for effective deliverables. Currently, there are many mathematical-based symmetric and asymmetric key cryptographic approaches and identity- and attribute-based public key cryptographic approaches that exist to address the abovementioned concerns and limitations such as computational overheads and taking more time for crucial generation as part of the encipherment and decipherment process for large-scale data privacy and security. In addition, the required key for the encipherment and decipherment process may be generated by a third party which may be compromised and lead to man-in-the-middle attacks, brute force attacks, etc. In parallel, there are some other quantum key distribution approaches available to produce keys for the encipherment and decipherment process without the need for a third party. However, there are still some attacks such as photon number splitting attacks and faked state attacks that may be possible with these existing QKD approaches. The primary motivation of our work is to address and avoid such abovementioned existing problems with better and optimal computational overhead for key generation, encipherment, and the decipherment process compared to the existing conventional models. To overcome the existing problems, we proposed a novel dynamic quantum key distribution (QKD) algorithm for critical public infrastructure, which will secure all cyber–physical systems as part of IIoT. In this paper, we used novel multi-state qubit representation to support enhanced dynamic, chaotic quantum key generation with high efficiency and low computational overhead. Our proposed QKD algorithm can create a chaotic set of qubits that act as a part of session-wise dynamic keys used to encipher the IIoT-based large scales of information for secure communication and distribution of sensitive information.

With the growing adoption of cloud computing, ensuring data security in cloud environments has become a critical concern for business organizations. Quantum cryptography utilizes the principles of quantum mechanics to guarantee secure communication, as any attempt to eavesdrop will change the quantum states, alerting the parties of the intrusion. This paper proposes a multi-qubit Quantum Key Distribution (QKD) ciphertext-policy attribute-based encryption (CP-ABE) for cloud security. The proposed multi-qubit QKD model for secure cloud data using quantum cryptography involves the use of a quantum key distribution protocol to generate a secure key for encryption and decryption. This protocol involves sending quantum signals through a quantum channel to distribute a secret key between the sender and the receiver. The key is then used for the encryption and decryption of data using the CP-ABE technique. This technique allows the encryption and decryption of data based on attributes rather than an explicit key exchange, making it particularly suitable for cloud environments where data is stored and processed by multiple users with varying levels of access. The positive results from the proposed simulation model suggest the potential of quantum cryptography in securing cloud data.

One of the most significant threats that hinder the effective functioning of existing models and technologies in 6G networks, particularly within the realms of Industry 5.0, is the lack of privacy and security. Traditional cryptographic models that were once considered secure have proven to be vulnerable to an expanding range of threats, such as quantum attacks. This vulnerability results in the exposure of sensitive data to unauthorized breaches and compromises the integrity and overall functionality of the network. Access control mechanisms implemented up to this point could not ensure that security compromises are prevented as secure data sharing is facilitated effortlessly. As a result, attempts to access sensitive data without authorization are often made, leaving the networks vulnerable to DDoS attacks that have the potential to disrupt their operation. Considering these pervasive challenges, our paper proposes an innovative hybrid protocol suite explicitly designed for 6G networks in Industry 5.0. Specifically, our approach integrates leading-edge technologies, such as Nth-degree Truncated polynomial Ring Unit Encryption with Supersingular Isogeny Diffie–Hellman (NTRUEncrypt-SIDH). and Key Policy attribute-based encryption (KP-ABE). It aims to address the vulnerabilities associated with traditional cryptographic models to ensure high confidentiality and authorized data sharing. Moreover, we implement modern DDoS mitigation techniques, such as rate limiting, traffic filtering, and adaptive routing, to enhance the resilience of the network and ensure that system operation remains uninterrupted. Experimental evaluations demonstrate the superiority of the proposed model over existing standards. Notably, the hybrid protocol achieves encryption and decryption times of 1.8 seconds and 1.667 seconds per GB, respectively, with a key generation time of 2.857 seconds per GB, ensuring efficient performance even in resource-constrained environments. The system achieves a total computational overhead of 11.7 milliseconds for KP-ABE combined with NTRUEncrypt-SIDH, outperforming other cryptographic combinations. Additionally, the model exhibits enhanced scalability, flexibility, and security, offering robust defenses against quantum and classical threats while ensuring seamless user policy-based access control. These results underscore the proposed hybrid cryptosystem's potential as a transformative standard for the secure and efficient operation of 6G networks in Industry 5.0.

Cloud computational service is one of the renowned services utilized by employees, employers, and organizations collaboratively. It is accountable for data management and processing through virtual machines and is independent of end users’ system configurations. The usage of cloud systems is very simple and easy to organize. They can easily be integrated into various storages of the cloud and incorporated into almost all available software tools such as Hadoop, Informatica, DataStage, and OBIEE for the purpose of Extraction-Transform-Load (ETL), data processing, data reporting, and other related computations. Because of this low-cost-based cloud computational service model, cloud users can utilize the software and services, the implementation environment, storage, and other on-demand resources with a pay-per-use model. Cloud contributors across this world move all these cloud-based apps, software, and large volumes of data in the form of files and databases into enormous data centers. However, the main challenge is that cloud users cannot have direct control over the data stored at these data centers. They do not even know the integrity, confidentiality, level of security, and privacy of their sensitive data. This exceptional cloud property creates several different security disputes and challenges. To address these security challenges, we propose a novel Quantum Hash-centric Cipher Policy-Attribute-based Encipherment (QH-CPABE) framework to improve the security and privacy of the cloud user’s sensitive data. In our proposed model, we used both structured and unstructured big cloud clinical data as input so that the simulated experimental results conclude that the proposal has precise, resulting in approximately 92% correctness of bit hash change and approximately 96% correctness of chaotic dynamic key production, enciphered and deciphered time as compared with conventional standards from the literature.

Early detection of Moderate Cognitive Impairment (MCI) and Alzheimer's disease (AD) is critical for increasing survival rates. Speech feature extraction is used in prior MCI and AD detection algorithms during neuropsychological assessments by medical specialists. The study's goal is to create an MCI and AD detection model using Automatic Speech Recognition (ASR) and a DL model. The suggested approach largely employs the Gaussian Mixture Model (GMM) for ASR in the patient's spontaneous speech. Furthermore, the Deep Belief Network (DBN) model is used to extract feature vectors from identified voice data. Finally, the SoftMax (SM) classifier is used to detect the presence of MCI and AD disorders in the used speech signals. A series of simulations were run to assess the superior performance of the GMM-DBN (Gaussian Mixture Model-Deep Belief Network model). Effectively describes the distribution of data observations as a weighted average of parameterized Gaussian distributions. The testing results indicated the GMM-DBN model's superior performance, with maximum accuracy of 90.28% and 86.76% on the binary and multiple class classifications, DN respectively. The GMM-DBN methodology has been successful in the classification of multiple classes, as evidenced by its F1-score reaching a maximum of 90.19% and its accuracy reaching 90.28%.