Strengthening Cryptographic Systems with AI-Enhanced Analytical Techniques

  • Authors

    https://doi.org/10.14419/fh79gr07

    Received date: March 27, 2025

    Accepted date: April 14, 2025

    Published date: April 24, 2025

  • Cryptography, Artificial Intelligence, Encryption, Security, AI-Enhanced Techniques

  • Abstract

     This study paper uses advanced Artificial Intelligence (AI) analytical tools to enhance cryptographic systems and counter evolving security threats. The proposed approach integrates traditional cryptographic techniques with Machine Learning (ML) to improve key management, encryption algorithms, and overall system security. This methodology is further strengthened by integrating the Cyber-Kill Chain (CKC) and the National Institute of Standards and Technology (NIST ) Cybersecurity Framework. In CKC’s stage model, Reconnaissance, Weaponization, and Exploitation are related to the NIST phases of Identifying, Protecting, Detecting, Responding, and Recovering as a comprehensive cybersecurity plan. Bayesian networks, Markov Decision Processes, and Partial Differential Equations (PDE) are referenced for threat detection, temporal modeling of vulnerabilities, and mathematical correctness, respectively. Introducing such optimizations promoted by AI into the CKC and NIST frameworks helps the proposed system achieve better flexibility, robustness, and extensibility. Additionally, reinforcement learning is explored to dynamically adjust security measures based on real-time threats. Experimental validation supports the efficiency of integrating AI-driven analytics into cryptographic frameworks. In this context, the work suggests a forward-looking plan for cybersecurity in contemporary society, mapping between theory development and applications that produce sound and secure cryptographic systems that neutralize cutting-edge security risks. 

  • References

    1. P. W. Shor, ”Algorithms for quantum computation: Discrete logarithms and factoring,” in Proceedings of the 35th Annual Symposium on Foundations of
    2. Computer Science, Santa Fe, NM, USA, 1994, pp. 124–134. [Online]. Available at: https://doi.org/10.1109/SFCS.1994.365700
    3. M. Mohammad, ”Artificial intelligence for cryptographic security in the post-quantum world,” Journal of Cryptography Research, vol. 45, no. 3, pp.
    4. –227, 2019. [Online]. Available at: https://doi.org/10.1007/s00145-019-9279-8
    5. R. Salinas, ”Enhancing encryption with machine learning: A step forward in post-quantum cryptography,” AI and Cybersecurity, vol. 10, no. 1, pp.
    6. –47, 2020. [Online]. Available at : https://doi.org/10.1145/3338903
    7. K. Gai, ”Neural networks in cryptographic algorithm optimization,” Neural Computing and Applications, vol. 17, no. 5, pp. 515–522, 2008. [Online].
    8. Available at: https://doi.org/10.1007/s00542-008-0530-3
    9. O. Regev, ”Lattice-based cryptography and its applications,” in Proceedings of the 50th Annual IEEE Symposium on Foundations of Computer Science,
    10. , pp. 1–9. [Online]. Available at : https://doi.org/10.1109/FOCS.2009.38
    11. D. Cai, ”AI-optimized lattice-based cryptographic protocols,” Journal of Cryptography, vol. 58, no. 1, pp. 49–67, 2021. [Online]. Available at :
    12. https://doi.org/10.1007/s00145-020-00325-w
    13. T. Cross et al., ”Qiskit: An open-source quantum computing software development framework,” IBM Journal of Research and Development, vol. 61, no.
    14. , pp. 1–10, 2017. [Online]. Available at: https://doi.org/10.1147/JRD.2017.2673310
    15. M. Mosca, ”Cybersecurity in an era with quantum computers: Will we be ready?,” IEEE Security and Privacy, vol. 16, no. 5, pp. 38–41, 2018. [Online].
    16. Available at: https://doi.org/10.1109/MSP.2018.3761723
    17. J. Hoffstein, ”Lattice-based cryptography and its role in quantum-safe security,” Advances in Cryptography, vol. 6, no. 4, pp. 135–148, 1998.
    18. C. Gentry, ”Fully homomorphic encryption using ideal lattices,” in Proceedings of the 41st Annual ACM Symposium on Theory of Computing, Bethesda,
    19. MD, USA, 2009, pp. 169–178. [Online]. Available at: https://doi.org/10.1145/1536414.1536440
    20. D. Boneh and R. J. Lipton, ”Quantum cryptanalysis of hidden linear functions,” in Advances in Cryptology-CRYPTO ’95, Proceedings of the
    21. th Annual International Cryptology Conference, Santa Barbara, CA, USA, Aug. 27–31, 1995, vol. 963, pp. 424–437. [Online]. Available at :
    22. https://doi.org/10.1007/3-540-44750-4 34
    23. D. J. Bernstein, J. Buchmann, and E. Dahmen, Eds., Post-Quantum Cryptography. Berlin, : Springer, 2009. [Online]. Available at: https://doi.org/10.
    24. /978-3-540-88702-7
    25. M. A. Nielsen and I. L. Chuang, Quantum Computation and Quantum Information. Cambridge, UK: Cambridge University Press, 2000.
    26. L. Chen, S. Jordan, Y.-K. Liu, D. Moody, R. Peralta, R. Perlner, and D. Smith-Tone, ”Report on post-quantum cryptography,” National Institute of
    27. Standards and Technology, Gaithersburg, MD, USA, NISTIR 8105, 2016. [Online]. Available at: https://doi.org/10.6028/NIST.IR.8105
    28. C. Peikert, ”A decade of lattice cryptography,” Foundations and Trends in Theoretical Computer Science, vol. 10, no. 4, pp. 283–424, 2016. [Online].
    29. Available at: https://doi.org/10.1561/0400000074
    30. G. Alagic, D. Apon, J. Cooper, Q. Dang, J. Kelsey, C. Miller, R. Peralta, R. Perlner, and D. Smith-Tone, ”Status report on the second round of the NIST
    31. post-quantum cryptography standardization process,” National Institute of Standards and Technology, Gaithersburg, MD, USA, NISTIR 8309, 2020.
    32. [Online]. Available at: https://doi.org/10.6028/NIST.IR.8309
    33. D. Gottesman, ”The Heisenberg representation of quantum computers,” in Proceedings of the 22nd International Colloquium on Group Theoretical
    34. Methods in Physics, 1998, pp. 32–43. [Online]. Available at: https://doi.org/10.1063/1.532707
    35. J. Xu, ”Advances in quantum-safe cryptographic algorithms,” IEEE Transactions on Information Theory, vol. 67, no. 5, pp. 3309–3324, 2021.
    36. T. Zhang and H. Li, ”Post-quantum cryptography: Recent developments and future challenges,” Journal of Cryptographic Engineering, vol. 10, no. 3,
    37. pp. 185–199, 2020.
    38. P. Wang, ”Machine learning methods in post-quantum cryptography,” IEEE Access, vol. 7, pp. 24068–24079, 2019.
    39. M. A. Khan, F. Afghah, and M. A. Abu-Shareha, ”An Improvised Certificate-Based Proxy Signature Using Hyperelliptic Curve Cryptography for
    40. Secure UAV Communications,” IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 1, pp. 123–135, Jan. 2025.
    41. I. Mustafa, A. Akhunzada, and S. Zeadally, ”Post-Quantum Cryptographic Communication Protocol,” U.S. Patent 10,581,604, Mar. 3, 2020. [Online].
    42. Available at: https://patents.google.com/patent/US20190116035A1/en
    43. A. Akhunzada, A. S. Al-Shamayleh, S. Zeadally, A. A. Abu-Shareha, and A. Almogren, ”Design and Performance of an AI-Enabled Threat Intelligence
    44. Framework for IoT-Enabled Autonomous Vehicles,” Computers and Electrical Engineering, vol. 119, p. 109609, Nov. 2024. [Online]. Available at:
    45. https://doi.org/10.1016/j.compeleceng.2024.109609

  • Downloads

  • How to Cite

    Chaganti, K., & Paidy, P. (2025). Strengthening Cryptographic Systems with AI-Enhanced Analytical Techniques. International Journal of Applied Mathematical Research, 14(1), 13-24. https://doi.org/10.14419/fh79gr07