Fears regarding the security of any data are increasing drastically with the rapid deployment of cloud-based systems based on artificial intelligence (AI) systems, as of now, owing to the existence of quantum computing, which is threatening the existing cryptography schemes. To ensure secure communication and smart threat detection in cloud networks, this paper proposes a hybrid cloud-based security framework, Quantum Shield, which will deploy Quantum Key Distribution (QKD) and Quantum Machine Learning (QML). The QKD module relies on the BB84 protocol when generating encryption keys, sharing the Quantum Bit Error Rate (QBER) to detect eavesdropping and is considered to be safe. At the same time, the QML layer manipulates TensorFlow Quantum-executed Variational Quantum Circuits (VQC) to perform the processes of classification on encrypted data. QKD and QML modules are also scalably deployed on cloud infrastructure and the system is evaluated and measured within a simulated environment. The experiments suggest the framework is effective in identifying interception attacks and containing QBER below the security threshold in case of normal conditions. It also classifies better than its traditional counterparts. Further studies will be focused on quantum operation and the validation on a large scale. The proposed approach demonstrates a quantum and scalable approach to protecting cloud AI systems.
Fears regarding the security of any data are increasing drastically with the rapid deployment of cloud-based systems based on artificial intelligence (AI) systems, as of now, owing to the existence of quantum computing, which is threatening the existing cryptography schemes. To ensure secure communication and smart threat detection in cloud networks, this paper proposes a hybrid cloud-based security framework, Quantum Shield, which will deploy Quantum Key Distribution (QKD) and Quantum Machine Learning (QML). The QKD module relies on the BB84 protocol when generating encryption keys, sharing the Quantum Bit Error Rate (QBER) to detect eavesdropping and is considered to be safe. At the same time, the QML layer manipulates TensorFlow Quantum-executed Variational Quantum Circuits (VQC) to perform the processes of classification on encrypted data. QKD and QML modules are also scalably deployed on cloud infrastructure and the system is evaluated and measured within a simulated environment. The experiments suggest the framework is effective in identifying interception attacks and containing QBER below the security threshold in case of normal conditions. It also classifies better than its traditional counterparts. Further studies will be focused on quantum operation and the validation on a large scale. The proposed approach demonstrates a quantum and scalable approach to protecting cloud AI systems.
The widespread cryptographic methods RSA and AES have become more susceptible to quantum-based attacks because researchers developed quantum algorithms such as Shor's and Grover's. QKD chromosomes from quantum mechanics grant authorized parties the ability to exchange cryptographic keys securely alongside built-in eavesdropping detection functionality. In the proposed method a Quantum Key Distribution (QKD) is used to integrate into cloud environments to secure data against quantum threats which have become crucial security concerns today. A new security framework for combining QKD with standard cloud utilities strengthens both access privacy and verify data integrity and ensure authentication of user information. The system framework consists of key generation from quantum elements followed by distribution through quantum channels to support secure communication protocols for Software as a Service (SaaS) and Platform as a Service (PaaS) as well as Infrastructure as a Service (IaaS). The key performance evaluation of the QKD system includes measurements of key generation rate together with Quantum Bit Error Rate (QBER) and eavesdropping detection accuracy rates. The analysis against traditional cryptographic systems proves that QKD stands superior in quantum attack resilience. The proposed system demonstrates its efficiency and robustness through simulation outcomes as well as visual representations. The presented work demonstrates both the revolutionary capability of quantum technology for cloud security along with a deployable system architecture for future systems implementation. The research work advances the creation of cloud infrastructure systems which combine high security resilience to protect against future cyber threats.
The rise of quantum computing poses threats to traditional cryptographic systems, thereby requiring security measures to safeguard against traditional and quantum-attacker cyber insecurity. This framework uses quantum machine learning (QML) algorithms along with quantum-safe encryption to boost security measures. The proposed system combines QML anomaly detection models variational quantum classifier (VQC) with quantum support vector classifier (QSVC) as well as quantum neural network and examines them based on the BB84 QKD protocol for information safety. This model was evaluated using three datasets of HIKARI Flow intrusion detection records, phishing activity logs, and malicious URLs. This includes all high-dimensional input by extensive application of feature engineering, which merges entropy scoring combined with keyword extraction and domain analysis to transmogrify it into suitable inputs for quantum processing. The QML models outperformed the traditional models with a maximum phishing detection accuracy of 97.75% by QSVC implementation. With the BB84 protocol, its eavesdropping detection was proved by quantum interference detection upon testing on IBM's Qiskit and Google's Cirq systems while the operations were secure and in attack scenarios. This system combines the latest features to address the limitations of the current AI security model and incorporates post-quantum cryptography to protect against quantum threats. In conclusion, QML and quantum cryptography work efficiently with operational cybersecurity platforms. Received: 31 December 2024 | Revised: 10 April 2025 | Accepted: 7 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Ramasubramaniyan Gunasridharan: Methodology. Ali Altalbe: Methodology. Bharathi Mohan Gurusamy: Conceptualization, Writing - review & editing. Gundala Pallavi: Writing - original draft. Prasanna Kumar Rangarajan: Supervision.
The rapid advancement of quantum computing poses a significant threat to conventional cryptographic systems that rely on computational complexity for security [3]. Quan-tum Key Distribution (QKD) offers a secure communication mechanism based on the principles of quantum mechanics, enabling the detection of unauthorized interception attempts [1]. However, practical QKD deployments remain vulnerable to channel noise, hardware imperfections, and sophisticated eavesdropping attacks [5], [6]. This paper presents an AI-driven threat detection and response framework that combines BB84 Quantum Key Distribution, IBM Qiskit simulation, and machine learning-based anomaly detection [9]. A high-fidelity quantum simulator is developed using IBM Qiskit and AerSimulator to model realistic quantum communication channels with depo-larizing noise. A Random Forest classifier trained on 50,000 simulated transmission sequences identifies malicious activities by analyzing Quantum Bit Error Rate (QBER), noise levels, sifted key lengths, and engineered heuristic features [7]. Experimental analysis demonstrates high detection accuracy while maintaining real-time operational capabilities. The proposed system further incorporates automated mitigation techniques including Privacy Amplification and E91 protocol migration to enhance quantum network resilience [2]. The results demonstrate that integrating artificial intelligence with quantum cryptography significantly improves threat detection, response efficiency, and overall communication security.
As quantum machine learning (QML) continues to evolve, it promises unparalleled computational advantages in processing complex data. However, the rise of QML also introduces critical concerns regarding data security and privacy, particularly in sensitive domains such as healthcare, finance, and defense. Classical cryptographic methods fall short in addressing threats that arise in quantum communication and computation environments. To bridge this gap, this paper presents a hybrid framework that integrates quantum cryptography—specifically Quantum Key Distribution (QKD)—with QML pipelines, ensuring end-to-end quantum-secure machinelearning operations. We first construct a theoretical model that outlines how QKD can be effectively embedded into a typical QML workflow to mitigate adversarial threats such as eavesdropping, model inversion, and poisoning attacks. We then implement this framework using IBM’s Qiskit and a simulated QKD environment via QuNetSim, applying it to a quantum support vector machine (qSVM) classifier. The integration is evaluated based on accuracy, computational overhead, and communication latency. Our results indicate that quantum-secured QML systems can maintain robust model performance while significantly enhancing data confidentiality. This work lays the groundwork for future developments in secure quantum artificial intelligence infrastructures.
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.
With the growing Internet of Things (IoT), many applications like traffic management, smart cities, environment monitoring, industrial automation and healthcare have been revolutionized. As the number of resourcelimited IoT devices grows and connects with each other, security issues have become more intricate and severe, such as unauthorized access, data tampering, replay attacks, and the rise in threat from quantum computing to traditional cryptographic methods. To solve those problems, this paper proposes Quantum Shield-IoT, which is a quantum-resilient hybrid security framework that combines the Quantum Key Distribution (QKD) protocol of BB84 with ASCON lightweight authenticated encryption, blockchain security, and cloud computing to ensure secure end-to-end data transmission in IoT. The BB84 protocol works both to generate quantum secure cryptographic keys, and to identify eavesdropping via Quantum Bit Error Rate (QBER) analysis. The generated keys are used by ASCON-128 to ensure efficient data confidentiality, integrity and authentication with low computation load, which makes it suitable for resourceconstrained IoT devices. Encrypted data are securely stored in the cloud while the data integrity and traceability of the SHA-256 hash values are ensured by being stored on the blockchain, making them irretrievable. Encrypted data are stored securely in the cloud, while the data integrity and traceability of the hash value ensure irretrievable storage through the blockchain. The proposed framework was implemented using IBM Quantum Qiskit, ASCON-128, blockchain platforms and cloud storage. Experimental results show that quantum bit error rate (QBER) varies from 2.1% to 3.5% for typical communication and 24.8% for eavesdropping attempts, proving reliable QKD. ASCON encryption range was between 8.2 ms and 69.5 ms, and blockchain verification was less than 25 ms for 1,000 transactions with more than 99% of the integrity accuracy. Moreover, the framework achieved an attack detection rate higher than 97.9% in several cyberattacks and hence provided a scalable, light-weight and quantum resistant security solution for next generation IoT ecosystems.