Abstract Machine-to-machine (M2M) communication has become an integral part of various industries, facilitating efficient data exchange and enabling autonomous decision-making processes. However, the proliferation of interconnected devices has brought about unprecedented security challenges. Conventional cryptographic methods may not be sufficient to protect sensitive data in the face of rapidly evolving cyber threats. To address this issue, a novel approach combining blockchain technology and deep quantum computing has been proposed to enhance the security and privacy of M2M communication. This paper presents an advanced blockchain-enabled deep quantum computing model designed to safeguard machine-to-machine communication from potential cyber threats. At the core of the model is a quantum key distribution (QKD) protocol, which enables the secure establishment of cryptographic keys between communicating devices. To ensure the scalability and immutability of the M2M communication network, a blockchain framework is integrated into the system. Additionally, the model incorporates deep learning techniques to bolster the security infrastructure further. In this paper we introduce an innovative solution to address the security challenges in machine-to-machine communication. By integrating advanced quantum computing, blockchain technology, and deep learning, the proposed model establishes a robust and secure framework that ensures the confidentiality, integrity, and authenticity of M2M data exchanges.
Abstract Machine-to-machine (M2M) communication has become an integral part of various industries, facilitating efficient data exchange and enabling autonomous decision-making processes. However, the proliferation of interconnected devices has brought about unprecedented security challenges. Conventional cryptographic methods may not be sufficient to protect sensitive data in the face of rapidly evolving cyber threats. To address this issue, a novel approach combining blockchain technology and deep quantum computing has been proposed to enhance the security and privacy of M2M communication. This paper presents an advanced blockchain-enabled deep quantum computing model designed to safeguard machine-to-machine communication from potential cyber threats. At the core of the model is a quantum key distribution (QKD) protocol, which enables the secure establishment of cryptographic keys between communicating devices. To ensure the scalability and immutability of the M2M communication network, a blockchain framework is integrated into the system. Additionally, the model incorporates deep learning techniques to bolster the security infrastructure further. In this paper we introduce an innovative solution to address the security challenges in machine-to-machine communication. By integrating advanced quantum computing, blockchain technology, and deep learning, the proposed model establishes a robust and secure framework that ensures the confidentiality, integrity, and authenticity of M2M data exchanges.
The rapid evolution of digital technologies has intensified the need for secure, transparent, and efficient digital ecosystems. Blockchain technology, known for its decentralized and tamper-resistant nature, has emerged as a promising solution for ensuring data integrity and trust. Simultaneously, Quantum Machine Learning (QML) is gaining attention for its potential to revolutionize computational capabilities by leveraging quantum computing principles. This paper explores the integration of blockchain and QML to design secure digital ecosystems capable of handling complex data processing and ensuring robust security. Blockchain provides a distributed ledger for secure data storage and transaction validation, while QML enhances analytical capabilities through advanced pattern recognition and optimization techniques. The proposed framework addresses key challenges such as data privacy, scalability, and computational efficiency. Additionally, the study examines the role of smart contracts in automating processes and ensuring compliance within decentralized environments. Security concerns, including quantum threats to classical cryptographic systems, are also discussed, along with potential solutions such as quantum-resistant cryptography. The findings indicate that the integration of blockchain and QML can significantly enhance the security, efficiency, and scalability of digital ecosystems, making them suitable for applications in finance, healthcare, and enterprise systems.
Blockchain technology, renowned for its ability to securely store data, hashes, and signatures permanently, faces unprecedented challenges in secure Consumer IoT (CIoT) networks with the advent of quantum computing. This paper proposes a set of robust quantum-based protocols and techniques to address these challenges by enhancing the security, scalability, and reliability of CIoT systems in the face of quantum threats. Updating the blockchain infrastructure is imperative to ensure ongoing security, which involves forks or protocol adjustments to establish new post-quantum chains and addresses, requiring rapid data and asset migration by users. Blockchain guarantees data integrity through an immutable ledger of transactions distributed via cryptographic hashes. The proposed quantum protocols and techniques enhance scalability and reliability and address the unique security needs of both commercial and governmental applications in secure CIoT networks through immersive embedded cyber-physical systems. These include Quantum Currency Security Protocols, Distributed Ledger Data Blocks, Quantum Ledger Verification, Quantum Solutions for Middleman Attacks, and the integration of Elliptic Curve Cryptography (ECC)-based security measures. By integrating these methods, the proposed approach ensures robust protection against emerging quantum threats, thereby securing sensitive information and transactions.
Rapid growth in IoT devices creates major security difficulties because they present potential weakening of systems to cyberattacks and open data breaches in addition to unauthorized system access. Traditional security methods relying on centralized authentication together with classical cryptographic techniques prove inadequate because they have single points of failure and exist under the threat of quantum computing advancement. A new system integrates blockchain technology and quantum cryptography according to this paper to resolve security issues. Data security through blockchain systems gives access to decentralized trust features together with tamper-proof records and ensures data integrity while quantum cryptographic methods especially Quantum Key Distribution (QKD) enable unconditional cryptographic key transfers and communications. A proposed security framework protects IoT networks through the integration of blockchain's unalterable system and QKD's quantum-resistant capabilities which create a strong defence system against classical and quantum-based threats. The approach includes three primary achievements: a system design that scales and performs security analyses together with performance tests that prove practicality. The presented research enables the development of next-generation IoT security measures suited for modern quantum technology advancements.
The convergence of blockchain technology and quantum-resistant artificial intelligence (AI) frameworks marks a significant advancement in cybersecurity. A blockchain-powered framework for quantum defensive AI aims to create robust, tamper-proof AI systems capable of withstanding the emerging threats posed by quantum computing. As quantum computers have the potential to break conventional cryptographic algorithms, integrating quantum-resistant cryptographic methods within a blockchain ensures data integrity and security. Due to being decentralized, blockchain makes it possible to see a ledger that is impossible to alter, which is required for monitoring AI actions and securing AI-powered applications. Here, blockchain is used so that data and models can be verified, their updates are secure, and everything can be audited. Machine learning is introduced to blockchain security to ensure that it remains protected from future quantum-style hacking attempts. Because of this integration, AI systems will be more secure, and the blockchain infrastructure will be better able to resist any future threats brought by quantum computers. It has been revealed by using blockchain technology with period defensive AI that both traditional and post-quantum transactions become more abundant with the passage of time. Out of every five transactions performed per second on a post-quantum computer, at least four utilize quantum principles and operate with much higher efficiency. This research is necessary to speed up the handling of transactions that need rapid attention. The choice of using a hybrid approach is sure to initiate a time when AI is highly secure and transparent, and it will be crucial in areas such as finance, healthcare, and the security of nations. Using these advanced technologies, it ensures that AI applications can remain stable against cyber threats that may exist in the future.
Generation of secure communication was getting known as a promising technology called as a quantum cryptography. Unfortunately, it still faces several challenges, most notably in terms of high computational demands, scalability limitations, quantum decoherence, and vulnerability to side channel attacks, such that deployment of it in real world remains impossible yet. This study shows how Machine Learning (ML) and Deep Learning (DL) can be applied to tune the mentioned obstacles so that quantum cryptographic frameworks become more secure, more efficient and more scalable. In particular, we introduce a hybrid AI enabled model where RL can be utilized for tuning the performance of the post quantum cryptographic algorithm implementations, GANs can be adopted for measuring the robustness of the system, and FL can be used to make the quantum key distribution scalable. Besides, this thesis uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to incorporate the quantum authentication and key exchange methods. Additionally, our methodology employs techniques based on Graph Neural Networks (GNNs) to achieve the best performance in the networks and Adversarial Machine Learning (AML) to counter and detect, and then reduce cyber threats in the run time. For preferable cryptographic computations, we introduce Quantum Neural Networks (QNNs) to reduce its dependency on expensive quantum hardware. Experimental results also show that ML/DL based quantum security frameworks provide lowering of compute burden, improvement in real time security of data, and improve resistance to cyber threats. The result of this roadmap based on AI driven strategy is a comprehensive view, with a high level of direction describing the main steps to lead to post quantum security and take benefit of quantum cryptography and apply it in a multitude of significant utilization including secure communication, cryptographic financial systems or national infrastructure protection.
The rapid adoption of cloud infrastructures has intensified concerns regarding data confidentiality, integrity, and trust management, particularly in the presence of emerging quantum computing threats. Classical cryptographic mechanisms, which underpin most existing cloud security architectures, are increasingly vulnerable to quantum attacks capable of compromising public-key encryption and key exchange protocols. To address these challenges, this paper proposes a hybrid security framework that integrates Quantum Cryptography with Blockchain technology to achieve robust, future-resistant data protection in cloud environments. The proposed framework leverages Quantum Key Distribution (QKD) to enable provably secure key generation and exchange based on quantum mechanical principles, ensuring resistance against eavesdropping and computational attacks. Blockchain technology is employed to provide decentralized trust, immutable audit trails, and transparent access control across distributed cloud services. Smart contracts enforce fine-grained security policies, automate authentication, and ensure tamper-proof data access logging. The synergy between quantum cryptography and blockchain establishes a secure key lifecycle, resilient identity management, and verifiable data integrity within multi-tenant cloud infrastructures. Comprehensive security analysis demonstrates that the proposed approach effectively mitigates quantum-enabled attacks, insider threats, and single-point-of-failure vulnerabilities commonly found in traditional cloud security models. Experimental evaluation and comparative analysis highlight improvements in data integrity assurance, key management robustness, and trust transparency, with acceptable computational and communication overhead. The results indicate that integrating quantum cryptography with blockchain presents a scalable and future-proof security paradigm for next-generation cloud infrastructures.