Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Electronic Security and Digital Forensics

Artificial intelligence empowered quantum-resistant cryptographic algorithms in information security of WSN for future-proof systems

Kiran Sree Pokkuluri, M. Kasiselvanathan N.A., N. Manoharan, Inderpreet Kaur, V. Bharathi N.A., R. Sivakumar N.A.

Published Jul 23, 2025• 0 citations
Publisher
DOI
10.1504/ijesdf.2027.10072341

Related papers

Quantum-Resistant Cryptographic Protocols Enhanced by AI- Driven Vulnerability Analysis for Future-Proof Enterprise Security

Zillay Huma

•Jan 1, 2025•0 citations

Artificial Intelligence and Quantum Cryptography

Petar Radanliev

•Jan 1, 2024•1 citation

Blockchain-Powered Framework Scheme for Quantum Defensive Artificial Intelligence

Manik Rakhra, Tiyas Sarkar, Gagandeep Singh Cheema

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.

Chapman & Hall/CRC eBooks•Apr 30, 2026•0 citations

Quantum Computing and AI Revolutionizing Cryptography and Security

Utpal Chakraborty

COJ Robotics & Artificial Intelligence•Feb 16, 2022•0 citations

117Chapter 5 Quantum-resistant cryptography in quantum twin shield architecture

Mohit Singh Bisht, Shivali Devi, Vaneet Kumar

The consideration to achieve strong data privacy data privacy and secure data transmission secure data transmission has become more important with the evolution of artificial intelligence that is ongoing. The continuous advancement of quantization technologies seriously threatens the routine cryptograph methods such as RSA and ECC, particularly in the cutting-edge AI systems with stringent requirements for data handling. In this section, a strong methodology for the inclusion of quantum-resistant cryptologic methodologies quantum-resistant cryptologic methodologies in AI processes is presented, which relies on lattice-based encryption lattice-based encryption , hash-based signatures hash-based signatures , and code-based key exchange code-based key exchange . The stated strategy realizes secured management, reliable data integrity, and user authentication in AI systems. Also, it mentions that cooperation between people who implement cryptography and people who are developing the solutions for AI is essential. To protect data, guarantee data confidentiality, and authenticate users in a connected world, this project explores various methods of quantum-resistant algorithm embedding in artificial intellectual infrastructures. Also, the devised framework emphasizes the importance of joint efforts from encryption experts, AI creators, and policymakers in the creation of efficient, resilient, and secure plans formulating a strong counter-response to both present and future quantum threats quantum threats . This research aims at providing a thorough procedure of quantum-resistant coding implementation quantum-resistant coding implementation which would guarantee the ability of AI systems to operate under security after quantum computers are constructed. The work also examines fundamental cryptographic protocols cryptographic protocols and their implementation in AI frameworks AI frameworks , while closely analyzing their role in constructing post-quantum defenses. The strategy is to promote interoperability from different domains, give priority to scaling, and maintain dynamic encryption standards while attempting to empower AI professionals to tackle the security issues in the post quantum era.

De Gruyter eBooks•Aug 23, 2025•0 citations

Artificial Intelligence Integrated with Bio-Quantum Computing to enhance data security and enhance encryption in virtual worlds

Pradeepkumar, S. Sheik Mydeen, Muhamed Fallahhusein +3 more

Artificial Intelligence (AI) combined with Bio-Quantum Computing is a revolutionary method of improving data security and encryption in virtual environments. Through the use of quantum mechanics and AI-optimized optimization, this combination strengthens cryptographic methods against future cyber threats. Current encryption algorithms like RSA and AES are vulnerable due to the advancement of Bio-Quantum Computing, which can decrypt conventional cryptographic algorithms. Additionally, traditional security systems are not able to adapt in real-time to advanced cyber-attacks. To meet these demands, the suggested framework combines AI with Quantum Key Distribution (AI -QKD). The QKD provides unbreakable key exchange cryptography using quantum properties, whereas AI optimizes encryption protocols in real time through threat analysis, providing heightened security resilience. This technique is used in virtual world transaction security, identity authentication, and secret communications. The AI-based mechanism flexibly adjusts encryption parameters in real time, preventing quantum attacks and increasing the integrity of the overall data. Findings reveal that the new approach enhances the robustness of encryption immensely, lowers risks of key compromise, and makes virtual interactions secure. The fusion of AI and QKD reflects high adaptability, making the encryption systems quantum-resistant and future-proof. The new approach achieves the data integrity by 98.70%, key compromise risks by 45.31%, adaptability by 97.63%.

International Conference on Computational Innovations and Engineering Sustainability (ICCIES)•Apr 24, 2025•0 citations
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.
The consideration to achieve strong data privacy data privacy and secure data transmission secure data transmission has become more important with the evolution of artificial intelligence that is ongoing. The continuous advancement of quantization technologies seriously threatens the routine cryptograph methods such as RSA and ECC, particularly in the cutting-edge AI systems with stringent requirements for data handling. In this section, a strong methodology for the inclusion of quantum-resistant cryptologic methodologies quantum-resistant cryptologic methodologies in AI processes is presented, which relies on lattice-based encryption lattice-based encryption , hash-based signatures hash-based signatures , and code-based key exchange code-based key exchange . The stated strategy realizes secured management, reliable data integrity, and user authentication in AI systems. Also, it mentions that cooperation between people who implement cryptography and people who are developing the solutions for AI is essential. To protect data, guarantee data confidentiality, and authenticate users in a connected world, this project explores various methods of quantum-resistant algorithm embedding in artificial intellectual infrastructures. Also, the devised framework emphasizes the importance of joint efforts from encryption experts, AI creators, and policymakers in the creation of efficient, resilient, and secure plans formulating a strong counter-response to both present and future quantum threats quantum threats . This research aims at providing a thorough procedure of quantum-resistant coding implementation quantum-resistant coding implementation which would guarantee the ability of AI systems to operate under security after quantum computers are constructed. The work also examines fundamental cryptographic protocols cryptographic protocols and their implementation in AI frameworks AI frameworks , while closely analyzing their role in constructing post-quantum defenses. The strategy is to promote interoperability from different domains, give priority to scaling, and maintain dynamic encryption standards while attempting to empower AI professionals to tackle the security issues in the post quantum era.
Artificial Intelligence (AI) combined with Bio-Quantum Computing is a revolutionary method of improving data security and encryption in virtual environments. Through the use of quantum mechanics and AI-optimized optimization, this combination strengthens cryptographic methods against future cyber threats. Current encryption algorithms like RSA and AES are vulnerable due to the advancement of Bio-Quantum Computing, which can decrypt conventional cryptographic algorithms. Additionally, traditional security systems are not able to adapt in real-time to advanced cyber-attacks. To meet these demands, the suggested framework combines AI with Quantum Key Distribution (AI -QKD). The QKD provides unbreakable key exchange cryptography using quantum properties, whereas AI optimizes encryption protocols in real time through threat analysis, providing heightened security resilience. This technique is used in virtual world transaction security, identity authentication, and secret communications. The AI-based mechanism flexibly adjusts encryption parameters in real time, preventing quantum attacks and increasing the integrity of the overall data. Findings reveal that the new approach enhances the robustness of encryption immensely, lowers risks of key compromise, and makes virtual interactions secure. The fusion of AI and QKD reflects high adaptability, making the encryption systems quantum-resistant and future-proof. The new approach achieves the data integrity by 98.70%, key compromise risks by 45.31%, adaptability by 97.63%.