The rapid adoption of Artificial Intelligence (AI) in consumer applications has significantly improved automation and user experiences. However, it has introduced emerging security challenges, particularly AI-driven adversarial attacks. Conventional cryptographic mechanisms are increasingly susceptible to these attacks and to the growing risks associated with quantum computing. To mitigate these challenges, this paper presents an AI-enabled, adversarial attack-resistant, post-quantum secure authentication protocol designed for consumer applications. The proposed protocol combines post-quantum lattice-based cryptographic techniques to provide strong and adaptive user authentication against evolving threats. Comprehensive formal security analysis and experimental evaluations confirm that the protocol effectively mitigates quantum and adversarial attacks, including classical attacks, while maintaining comparable computational and communication overhead with the existing competing authentication schemes. As a result, the proposed scheme becomes ideal for resource-constrained consumer environments, such as smart devices, Internet of Things (IoT) systems, and mobile platforms. Finally, an experimental investigation on radio frequency fingerprinting for consumer devices is conducted using an AI-driven model called TinyMLP, and our model achieves ≈ 97% authentication accuracy by resisting adversarial attacks.
The rapid adoption of Artificial Intelligence (AI) in consumer applications has significantly improved automation and user experiences. However, it has introduced emerging security challenges, particularly AI-driven adversarial attacks. Conventional cryptographic mechanisms are increasingly susceptible to these attacks and to the growing risks associated with quantum computing. To mitigate these challenges, this paper presents an AI-enabled, adversarial attack-resistant, post-quantum secure authentication protocol designed for consumer applications. The proposed protocol combines post-quantum lattice-based cryptographic techniques to provide strong and adaptive user authentication against evolving threats. Comprehensive formal security analysis and experimental evaluations confirm that the protocol effectively mitigates quantum and adversarial attacks, including classical attacks, while maintaining comparable computational and communication overhead with the existing competing authentication schemes. As a result, the proposed scheme becomes ideal for resource-constrained consumer environments, such as smart devices, Internet of Things (IoT) systems, and mobile platforms. Finally, an experimental investigation on radio frequency fingerprinting for consumer devices is conducted using an AI-driven model called TinyMLP, and our model achieves ≈ 97% authentication accuracy by resisting adversarial attacks.
The emergence of quantum computing is expected to break many conventional cryptographic algorithms currently used for authentication and secure communication. Existing security mechanisms in Next Generation Networks (NGNs), including 5G, IoT, smart healthcare, and cloud-based systems, are increasingly vulnerable to future quantum attacks. This research presents a lightweight and efficient Post-Quantum Cryptography (PQC) based authentication framework specifically designed for next generation network environments. The proposed framework combines quantum-resistant cryptographic techniques with optimized authentication procedures to provide secure identity verification, confidential communication, and attack resistance with minimal computational complexity. The model emphasizes reduced overhead, faster authentication response, and scalability for resource-constrained devices. Security analysis shows that the framework can effectively defend against replay attacks, impersonation attacks, session hijacking, and quantum-based cryptanalytic threats. Experimental evaluation indicates that the proposed approach achieves improved security performance while maintaining low latency and reduced resource consumption, making it suitable for practical deployment in future intelligent communication networks.
The rapid expansion of Internet of Things (IoT) ecosystems has introduced significant security challenges due to resource-constrained devices, heterogeneous network architectures, and largescale connectivity. Traditional authentication and key exchange mechanisms, primarily based on classical cryptographic algorithms such as RSA and ECC, are vulnerable to future quantum computing attacks. The emergence of quantum algorithms, particularly Shor’s algorithm, threatens the long-term confidentiality and integrity of IoT communications. This paper proposes a Quantum-Resistant Authentication and Key Exchange Protocol specifically designed for secure IoT architectures. The proposed framework integrates post-quantum cryptographic primitives, including lattice-based key exchange mechanisms and hash-based authentication schemes, optimized for low computational overhead suitable for constrained IoT devices. The protocol ensures mutual authentication, forward secrecy, and resistance against quantum adversaries while maintaining minimal communication latency. Security analysis demonstrates robustness against replay, man-in-the-middle, and key-compromise attacks. Performance evaluation shows that the proposed solution achieves a balance between quantum security strength and resource efficiency, making it suitable for next-generation IoT deployments in smart cities, healthcare systems, and industrial automation.
With the rapid advancement of artificial intelligence (AI) and quantum computing, cybersecurity threats have evolved, giving rise to adversarial quantum attacks. These attacks exploit the vulnerabilities of AI models using quantum algorithms, posing a significant risk to data security, model robustness, and decision-making systems. This paper presents a comprehensive review of adversarial quantum attacks on AI, analyzing their mechanisms, potential impacts, and countermeasures. It explores how quantum computing can enhance adversarial attacks by accelerating the generation of adversarial examples, breaking cryptographic protections, and undermining AI model integrity. Additionally, the study examines different attack vectors, including quantum-enhanced adversarial perturbations, quantum machine learning (QML) vulnerabilities, and quantum decryption threats. The paper also discusses defensive strategies such as quantum-resistant AI models, quantum cryptographic defenses, and hybrid quantum-classical security frameworks that can mitigate these risks. By evaluating existing research and emerging trends, this review provides insights into the growing intersection of AI security and quantum computing. The findings emphasize the urgent need for robust quantum-aware AI security frameworks to safeguard AI-driven systems in the quantum era. Future research directions include developing quantum-adaptive AI models, post-quantum cryptographic techniques, and real-world applications of quantum-safe AI architectures. This review aims to contribute to the ongoing discourse on ensuring AI resilience against adversarial quantum threats, paving the way for a secure and quantum-resistant future.
As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.
The rapid proliferation of IoT technology has brought billions of devices into play that need to operate in a variety of diverse, resource-constrained environments. Secure authentication among these devices is a critical challenge, as traditional certificate-based public key infrastructures incur heavy computation and management overhead. Moreover, recent breakthroughs in quantum computing put at risk the security of classical cryptographic algorithms commonly used in IoT systems. This paper proposes a post-quantum certificateless authentication framework designed to address the efficiency-long-term security requirements of IoT environments. The proposed framework uses lattice-based cryptography to resist quantum-enabled adversaries while eliminating the need for digital certificates. The certificateless approach evades the problem of key escrow of identity-based cryptography and simplifies the key management process. A lightweight authentication protocol, which is suitable for constrained IoT devices, is developed. Security analyses prove that our proposed framework resists common attacks such as impersonation, replay, and man-in-the-middle attacks. The performance evaluation shows that our proposed framework reduces the communication and computational overheads compared with the traditional certificatebased authentication schemes. In this way, it is appropriate for large-scale and futureproof deployments of IoT systems.
The digital banking ecosystem faces unprecedented security challenges with the imminent advent of quantum computing, which threatens to compromise existing cryptographic infrastructures. Traditional multi-factor authentication (MFA) systems in banking rely on classical cryptographic primitives vulnerable to quantum attacks, creating critical security gaps in financial transactions and customer data protection. This paper introduces a novel Quantum-Enhanced Multi-Factor Authentication Framework (QE-MFAF) specifically designed for digital banking systems, integrating lattice-based post-quantum cryptography with biometric fuzzy commitment schemes and behavioral analytics. The proposed framework incorporates a hybrid authentication mechanism combining quantum-resistant cryptographic protocols, continuous behavioral pattern recognition using deep learning models, and secure multi-party computation for transaction verification. Experimental validation on a synthetic banking dataset demonstrates superior performance with 99.7% authentication accuracy, 0.02% false positive rate, and 15ms average authentication latency while maintaining quantum resistance. The framework successfully mitigates advanced persistent threats, insider attacks, and quantum-based cryptographic vulnerabilities while ensuring seamless user experience in high-transaction banking environments. Performance comparisons with existing banking authentication systems show 34% improvement in security metrics and 28% reduction in computational overhead.