This quantitative study investigated the performance, efficiency, and security resilience of quantum-resistant cryptographic protocols integrated with artificial intelligence (AI) across cloud and Internet of Things (IoT) environments. The research aimed to empirically assess whether AI-enhanced cryptographic systems could outperform conventional post-quantum algorithms in encryption throughput, latency, resource optimization, and security robustness. A factorial experimental design was implemented, encompassing multiple algorithmic classes—lattice-based, hash-based, code-based, and multivariate polynomial systems—under both AI-integrated and non-AI configurations. The analysis incorporated 7,200 experimental runs executed under varying workloads, environments, and simulated attack conditions. Linear mixed-effects models, correlation analysis, and reliability testing were used to validate the statistical integrity of the results. The descriptive analysis indicated that AI-augmented frameworks achieved consistently higher encryption speeds, lower decryption latency, and superior throughput-adjusted security efficiency compared to traditional post-quantum systems. Correlation analysis revealed strong positive relationships between AI detection accuracy, encryption performance, and system stability, confirming that AI optimization significantly improved operational consistency. Reliability and validity tests showed high internal consistency, with Cronbach’s alpha coefficients exceeding 0.90, and factor analysis confirmed that performance indicators loaded strongly on the intended theoretical constructs of cryptographic performance and AI adaptability. Collinearity diagnostics verified the independence of predictors, with all variance inflation factors below 2.0. Regression analysis demonstrated that AI integration was a statistically significant predictor of improved cryptographic outcomes (p < 0.001), increasing throughput efficiency by over 14% on average while reducing latency and energy consumption. The findings confirmed the primary hypothesis that AI-driven cryptographic optimization enhances both computational efficiency and system resilience against classical and quantum attack simulations. Lattice-based and code-based cryptosystems showed the most substantial performance gains when combined with AI learning models. Overall, the results validated that intelligent, adaptive encryption frameworks achieve measurable, statistically significant advantages in performance, scalability, and security across both cloud and IoT domains. These findings provide empirical evidence supporting the integration of AI-based decision systems into post-quantum cryptography for secure and sustainable digital infrastructures.
This quantitative study investigated the performance, efficiency, and security resilience of quantum-resistant cryptographic protocols integrated with artificial intelligence (AI) across cloud and Internet of Things (IoT) environments. The research aimed to empirically assess whether AI-enhanced cryptographic systems could outperform conventional post-quantum algorithms in encryption throughput, latency, resource optimization, and security robustness. A factorial experimental design was implemented, encompassing multiple algorithmic classes—lattice-based, hash-based, code-based, and multivariate polynomial systems—under both AI-integrated and non-AI configurations. The analysis incorporated 7,200 experimental runs executed under varying workloads, environments, and simulated attack conditions. Linear mixed-effects models, correlation analysis, and reliability testing were used to validate the statistical integrity of the results. The descriptive analysis indicated that AI-augmented frameworks achieved consistently higher encryption speeds, lower decryption latency, and superior throughput-adjusted security efficiency compared to traditional post-quantum systems. Correlation analysis revealed strong positive relationships between AI detection accuracy, encryption performance, and system stability, confirming that AI optimization significantly improved operational consistency. Reliability and validity tests showed high internal consistency, with Cronbach’s alpha coefficients exceeding 0.90, and factor analysis confirmed that performance indicators loaded strongly on the intended theoretical constructs of cryptographic performance and AI adaptability. Collinearity diagnostics verified the independence of predictors, with all variance inflation factors below 2.0. Regression analysis demonstrated that AI integration was a statistically significant predictor of improved cryptographic outcomes (p < 0.001), increasing throughput efficiency by over 14% on average while reducing latency and energy consumption. The findings confirmed the primary hypothesis that AI-driven cryptographic optimization enhances both computational efficiency and system resilience against classical and quantum attack simulations. Lattice-based and code-based cryptosystems showed the most substantial performance gains when combined with AI learning models. Overall, the results validated that intelligent, adaptive encryption frameworks achieve measurable, statistically significant advantages in performance, scalability, and security across both cloud and IoT domains. These findings provide empirical evidence supporting the integration of AI-based decision systems into post-quantum cryptography for secure and sustainable digital infrastructures.
The accelerating development of quantum computing creates substantial cybersecurity challenges for cloud and Internet of Things (IoT) environments that continue to depend on quantum-vulnerable public-key cryptography. This study developed and quantitatively evaluated an AI-driven quantum-resistant security framework for accelerating the migration of U.S. cloud and IoT systems toward National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standards. The final analytical environment included 52 organizations, 164 cloud environments, 18,764 IoT devices, and 139,684 retained cryptographic assets, with 98.7% data completeness and 97.9% inventory-validation accuracy. Among retained assets, 62.1% contained quantum-vulnerable dependencies, including 34.6% associated with RSA, 20.0% with elliptic-curve cryptography, and 7.5% with Diffie–Hellman, while 63.2% of certificates remained quantum vulnerable. AI-assisted cryptographic discovery achieved 97.4% asset coverage and 97.9% vulnerability-detection sensitivity while reducing assessment time by 87.8%. XGBoost achieved the strongest quantum-risk classification performance, with 97.6% accuracy, 97.5% F1-score, 0.994 ROC-AUC, and 0.992 PR-AUC. Experimental evaluation of ML-KEM, ML-DSA, and SLH-DSA showed that pure PQC increased cloud secure-session latency by 23.8% and reduced throughput by 10.0%, whereas hybrid configurations increased latency by 39.5% and reduced throughput by 15.6%. AI-assisted migration increased implementation success from 84.6% to 97.1%, reduced remediation time by 60.2%, increased migration throughput by 151.1%, and reduced unresolved quantum-vulnerable exposure from 15.4% to 2.9%. High-security-maturity organizations achieved 98.8% implementation success compared with 87.8% among low-maturity organizations. Validation confirmed strong generalizability, with XGBoost achieving 97.1% cross-validation accuracy, 96.9% independent holdout accuracy, and 95.9% temporal-validation accuracy. Overall, the findings demonstrated that integrating AI-assisted cryptographic discovery, quantum-risk classification, migration prioritization, PQC performance assessment, cryptographic agility, interoperability testing, and organizational readiness provided a robust quantitative framework for strengthening post-quantum security across heterogeneous U.S. cloud and IoT infrastructures.
The rapid expansion of the Internet of Things (IoT) has introduced unprecedented cybersecurity challenges, as traditional cryptographic methods struggle to provide both efficiency and quantum resistance in resource-constrained environments. This research introduces Adaptive Quantum Cryptography (AQC), a novel security framework that integrates Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), Quantum Random Number Generators (QRNGs), and AI-driven dynamic adaptation to safeguard IoT networks against evolving cyber threats. Unlike classical encryption techniques such as RSA and ECC, which are vulnerable to quantum attacks, AQC leverages quantum mechanical principles—such as superposition, entanglement, and the no-cloning theorem—to ensure provably secure key exchange and data encryption. The proposed AQC system dynamically adjusts cryptographic protocols based on real-time network conditions, computational resources, and threat intelligence, optimizing security without imposing excessive overhead. Experimental evaluations demonstrate that AQC achieves a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum attacks compared to conventional encryption methods. Additionally, the integration of QRNGs enhances cryptographic key entropy, while AI-driven anomaly detection enables proactive threat mitigation. This framework is particularly suited for next-generation IoT applications, including smart grids, autonomous vehicles, and industrial automation, where security, scalability, and energy efficiency are critical. By combining quantum-resistant algorithms with adaptive security mechanisms, AQC provides forward secrecy, resistance to quantum adversaries, and robust authentication, ensuring long-term protection for IoT ecosystems in the post-quantum era. The findings underscore AQC’s potential as a foundational security architecture for future quantum-safe IoT networks.
The rapid proliferation of Internet of Things (IoT) devices has revolutionized digital communication, yet it simultaneously poses unprecedented security challenges due to constrained device capabilities and increasing cyber threats. Conventional cryptographic techniques, while currently effective, are vulnerable to emerging quantum computing technologies that threaten to render classical encryption obsolete. This paper explores the integration of quantum-safe cryptographic techniques within IoT networks to ensure long-term confidentiality, integrity, and authentication. It examines post-quantum cryptographic primitives, including lattice-based, hash-based, multivariate, and code-based schemes, and evaluates their suitability for resource-constrained IoT environments. The study focuses on lightweight implementation strategies, balancing computational efficiency with robust security requirements. Simulation analyses demonstrate that properly optimized quantum safe protocols can provide resilient encryption, key exchange, and digital signature functionalities without overwhelming device resources. Furthermore, the integration of these techniques with IoT communication standards and secure network protocols ensures end-to-end protection across heterogeneous networks. Challenges such as latency, energy consumption, and scalability are addressed, highlighting the necessity of tailored cryptographic solutions for diverse IoT applications. The findings confirm that quantum safe cryptography is essential for future-proofing IoT networks, offering sustainable security measures that remain resilient against both classical and quantum adversaries, thereby supporting the continued expansion of secure and intelligent interconnected systems.
As the digital landscape evolves, the reliance on cloud computing for critical infrastructure across various sectors highlights the need for robust security mechanisms to protect sensitive data from emerging cyber threats, particularly those posed by quantum computing. Traditional cryptographic systems, while currently effective, are vulnerable to quantum attacks, necessitating the development of quantum-resistant solutions. This research introduces the Quantum-Resilient Cryptographic Framework (QRCF), a hybrid, adaptive cryptographic framework designed to safeguard cloud environments against both classical and quantum threats. The QRCF integrates lattice-based Kyber and code-based McEliece algorithms, offering a comprehensive and scalable solution for secure data storage, management, and transmission. Key contributions include the development of a dynamic security management layer that adapts to real-time threat analysis, ensuring continuous protection against evolving threats, and the implementation of robust post-quantum cryptographic methods that maintain high performance and low computational overhead. Quantitative analysis shows that the QRCF maintains a high throughput of 420 MB/s for encryption and 400 MB/s for decryption under normal operations, with latency as low as 3.2 ms and 3.6 ms, respectively. The framework exhibited strong resistance to various attack models, with success rates of 1.0% for brute-force attacks, 1.875% for MITM attacks, 0.833% for side-channel attacks, and 1.6% for replay attacks. Against quantum threats, the QRCF showed no vulnerability to Shor’s Algorithm and a minimal success rate of 0.667% for Grover’s Algorithm. The framework’s design ensures seamless integration with existing cloud infrastructures, providing practical migration strategies to quantum-safe cryptography without disrupting operational workflows. By addressing key research gaps in quantum-safe cloud security, this study contributes significantly to the field, offering a robust, scalable, and efficient cryptographic solution that enhances the security and operational performance of cloud environments in the face of advancing quantum computational capabilities.
One of the promising solutions to overcome this quantum computing attack on conventional encryption protocol is the integration of Artificial Intelligence (AI) withPost Quantum Cryptography (PQC).In this research, AI-driven approaches are investigated in improving post quantum cryptographic key exchange protocol by being efficient, secure and scalable.Based on lattice based cryptography resistant against quantum [2], this work makes use of machine learning models in order to assist in optimizing, or predicting optimal parameter choices, for better key exchange in practice.The system proposed consists of the Learning with Errors (LWE) problem for increased security [4] and a hybrid AI model that minimizes the computation overhead in key exchange protocols [3].The system dynamically adjusts cryptographic parameters based on the side channel attacks that are feasible and up to date, making the system resilient to side channel attacks, and also reduces the latency of secure communication through the incorporation of deep learning algorithms.The key findings show that AI driven model significantly improves the key exchange speed by 27 percent over traditional PQC techniques, and still provides robust security guarantees [5].To sum up, this research brings a significant advancement in PQC protocols by combining the AI techniques to predict and defend against cryptographic VTEs [7].In the future, this approach will be generalized to other quantum-resistant protocols (including SIKE Narayana Gaddam https://iaeme.com/Home/journal/IJCET
The integration of Artificial Intelligence (AI) in Internet of Things (IoT) financial systems has ushered in a new era of autonomous decision-making, real-time analytics, and dynamic financial interactions. However, the advent of quantum computing presents an imminent threat to the cryptographic foundations of these systems, jeopardizing data confidentiality, transaction integrity, and system authenticity. This paper explores quantum-resistant cryptographic mechanisms—particularly lattice-based, hash-based, and multivariate polynomial cryptography—as viable defenses against quantum attacks on AI-powered IoT financial networks. The study reviews the vulnerabilities introduced by quantum algorithms such as Shor’s and Grover’s, assesses the performance and scalability of post-quantum cryptographic (PQC) protocols in real-world IoT financial deployments, and presents an optimized framework that balances cryptographic strength with computational feasibility. A hybrid security architecture is proposed, integrating PQC with AI-based anomaly detection to ensure end-to-end trust, resilience, and compliance with emerging quantum-secure standards. Through simulations and case-based evaluations, the findings underscore the critical importance of immediate cryptographic transition planning to safeguard future financial infrastructures.