Abstract :

Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography.

Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed.

Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study.

Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.

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Keywords :

artificial intelligence (AI), Cryptanalysis, Deep Learning (DL), Machine Learning (ML), Post-Quantum Cryptography (PQC), Quantum Computing, Side-Channel Attacks.

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References :

1. Bernstein, D. J., & Lange, T. (2017). Post-quantum cryptography. Nature Reviews Physics, 1(7), 446–448.
2. Pirandola, S., et al. (2020). Advances in quantum cryptography. Advances in Optics and Photonics, 12(4), 1012–1236.
3. Rivest, R. L., Shamir, A., & Adleman, L. (1978). A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2), 120–126.
4. Shor, P. W. (1994). Algorithms for quantum computation: Discrete logarithms and factoring. Proceedings of 35th FOCS, 124–134.
5. (2024). Post-Quantum Cryptography Program. https://csrc.nist.gov/Projects/post-quantum-cryptography
6. Alagic, G., et al. (2020). Status report on the second round of the NIST post-quantum cryptography standardization process. NIST IR 8309.
7. Bos, J., et al. (2018). CRYSTALS – Kyber: A CCA-secure module-lattice-based KEM. IEEE EuroS&P 2018, 353–367.
8. Ducas, L., et al. (2018). CRYSTALS-Dilithium: A lattice-based digital signature scheme. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2018(1), 238–268.
9. McEliece, R. J. (1978). A public-key cryptosystem based on algebraic coding theory. DSN Progress Report 42-44, 114–116. JPL.
10. Ning, Z., et al. (2020). A survey on multivariate public key cryptography. Symmetry, 12(10), 1642.
11. Buchmann, J., et al. (2011). XMSS – A practical forward secure signature scheme based on minimal security assumptions. In Post-Quantum Cryptography, LNCS 7071, 117–129. Springer.
12. Bernstein, D. J., & Lange, T. (2008). Attacking and defending the McEliece cryptosystem. In Post-Quantum Cryptography, LNCS 5299. Springer.
13. Peikert, C. (2016). A decade of lattice cryptography. Foundations and Trends in Theoretical Computer Science, 10(4), 283–424.
14. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
15. Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
16. Lyu, H., & Yu, Y. (2020). Artificial intelligence and cybersecurity: A comprehensive review. IEEE Access, 8, 144594–144607.
17. Radanliev, P., et al. (2020). Artificial intelligence and machine learning in dynamic cyber risk analytics at the edge. SN Applied Sciences, 2, 1678.
18. Sun, Y., Wu, Y., & Ma, J. (2021). Machine learning in cryptography: Challenges and opportunities. IEEE Access, 9, 150297–150312.
19. Aich, S., & Lai, J. C. (2021). Artificial intelligence and machine learning for future cryptography: A comprehensive survey. Sensors, 21(17), 5894.
20. Büyükkaya, E., & Güler, A. (2022). A review of AI-assisted post-quantum cryptographic key generation and management. Journal of Information Security, 13(4), 245–262.
21. Guo, Q., et al. (2023). Survey of AI-enhanced cryptographic protocol design. ACM Computing Surveys, 55(10), Article 213.
22. Mosca, M. (2018). Cybersecurity in an era with quantum computers: Will we be ready? IEEE Security & Privacy, 16(5), 38–41.
23. IBM Quantum. (2023). IBM Quantum Computing roadmap. https://www.ibm.com/quantum-computing/ See also: Bravyi, S., et al. (2022). The future of quantum computing with superconducting qubits. Journal of Applied Physics, 132(16), 160902.
24. IEEE Xplore Digital Library. (2025). https://ieeexplore.ieee.org
25. Chen, L. K., et al. (2016). Report on post-quantum cryptography. NISTIR 8105.
26. Schneier, B. (2015). Applied Cryptography: Protocols, Algorithms, and Source Code in C. John Wiley & Sons.
27. Moher, D., et al. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097.
28. Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE-2007-01. Keele University.
29. Regev, O. (2009). On lattices, learning with errors, random linear codes, and cryptography. Journal of the ACM, 56(6), Article 34.
30. Peikert, C. (2014). Lattice cryptography for the Internet. In Post-Quantum Cryptography 2014, LNCS 8772, 197–219. Springer.
31. Zhang, R., & Li, R. (2019). Parameter optimization of lattice-based cryptography using genetic algorithm. Proceedings of ICCCBDA 2019, 396–400. IEEE.
32. Gao, Y., & Li, J. (2022). Machine learning assisted parameter selection for learning with errors (LWE) problems. Information Sciences, 590, 211–228.
33. López-Rubio, E., et al. (2022). Reinforcement learning for adaptive post-quantum cryptography deployment. Expert Systems with Applications, 198, 116776.
34. Montanaro, A. (2016). Quantum algorithms: An overview. npj Quantum Information, 2, 15023.
35. Real, E., et al. (2020). Automl-zero: Evolving machine learning algorithms from scratch. Proceedings of ICML 2020, 8007–8019. PMLR.
36. Al-Momani, F., Hassan, A., & Zhang, Q. (2023). AI-based optimization of lattice parameters for post-quantum cryptography. IEEE Access, 11, 77423–77435.
37. Micciancio, D., & Regev, O. (2009). Lattice-based cryptography. In Post-Quantum Cryptography, 147–191. Springer.
38. Chen, Y., Liu, F., & Li, H. (2020). Machine learning based cryptanalysis: A survey. Journal of Cryptographic Engineering, 10(2), 115–132.
39. Das, S., & Ray, S. (2023). A survey on machine learning-based cryptanalysis of post-quantum schemes. ACM Computing Surveys, 55(11), 1–38.
40. Guo, Y., & Johansson, T. (2020). Lattice-based cryptanalysis using machine learning techniques. IEEE Transactions on Information Forensics and Security, 15(4), 1873–1886.
41. Gohr, M. (2019). Machine learning cryptanalysis of reduced-round SPECK. Advances in Cryptology – CRYPTO 2019, 1–25. Springer.
42. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
43. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
44. Zaid, E. M., & Aissa, S. (2021). Unsupervised machine learning for side-channel analysis: A review. Journal of Cryptographic Engineering, 11(3), 209–224.
45. Gilmore, R., et al. (2015). Neural network based attack on a masked implementation of AES. IEEE HOST 2015, 106–111.
46. Mangard, S., Oswald, E., & Popp, T. (2007). Power Analysis Attacks: Revealing the Secrets of Smart Cards. Springer.
47. Picek, S., Vredenda, D., & Schramm, K. (2020). Machine learning aided side-channel analysis: A survey. IEEE Transactions on Information Forensics and Security, 15, 1761–1775.
48. Wu, J., & Xu, Z. (2022). AI-based side-channel attack detection for post-quantum cryptographic implementations. Journal of Cryptographic Engineering, 12(2), 151–163.
49. Maghrebi, H., Portigliatti, T., & Prouff, E. (2016). Breaking cryptographic implementations using deep learning techniques. SPACE 2016, LNCS 10076, 3–26. Springer.
50. Benadjila, R., et al. (2020). Deep learning for side-channel analysis and introduction to ASCAD database. Journal of Cryptographic Engineering, 10(2), 163–188.
51. Bhattacharya, S., & Mukhopadhyay, D. (2022). Deep learning aided power analysis attacks on Kyber and Dilithium. Journal of Cryptographic Engineering, 12(3), 215–231.
52. Kim, J., et al. (2022). Novel side-channel attacks on lattice-based PQC implementations. IEEE Access, 10, 88109–88120.
53. Popović, M., Gajić, Z., & Stanković, R. S. (2019). Hardware implementations of post-quantum cryptography: Challenges and solutions. Electronics, 8(11), 1276.
54. Oder, T., et al. (2017). Practical CCA2-secure and masked ring-LWE implementation. IACR Transactions on Cryptographic Hardware, 2018(1), 142–174.
55. Bogdanov, A., & Vaudenay, S. (2019). Lightweight cryptography and its applications. ICISC 2019, 1–17. Springer.
56. [Zhao, L., & Keller, M. (2024). Reinforcement learning-assisted hardware acceleration for PQC on FPGAs. Journal of Cryptographic Hardware and Embedded Systems, 4(2), 98–117.
57. Bansal, G., & Kumar, N. (2023). Quantum-resistant security for industrial IoT using lattice-based cryptography and reinforcement learning. IEEE Transactions on Industrial Informatics, 19(4), 5891–5902.
58. Ravi, P., et al. (2020). Side-channel and fault-injection attacks over lattice-based post-quantum schemes (Kyber, Dilithium): Survey and new results. IACR Cryptology ePrint Archive 2019/916.
59. Radanliev, P. (2024). Quantum noise filtering in QKD systems using neural network ensembles. Future Generation Computer Systems, 152, 112–124.
60. Gaddam, S. C., Hua, M., & Doddapaneni, S. (2021). Accelerating post-quantum key exchange with hybrid deep learning models. Proceedings of IEEE ICC 2021, 1–6.
61. Biggio, B., & Roli, F. (2018). Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84, 317–331.
62. Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks. IEEE S&P 2017, 39–57.
63. Singh, R., & Prakash, V. (2025). Defending AI-driven cryptographic systems against adversarial attacks: A XAI approach. ACM Transactions on Privacy and Security, 28(1), Article 3.
64. Guidotti, R., et al. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1–42.
65. Arrieta, A. B., et al. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115.
66. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence. IEEE Access, 6, 52138–52160.
67. Han, S., Pool, J., Tran, J., & Dally, W. J. (2015). Learning both weights and connections for efficient neural networks. NeurIPS 2015, 1135–1143.
68. Barker, E., et al. (2020). Recommendation for key management – Part 1: General. NIST Special Publication 800-57.
69. Mosca, M., & Piani, M. (2019). Quantum threat timeline. GlobalRisk Institute Report.
70. Huang, L., & Zhang, Y. (2023). Privacy-preserving machine learning via post-quantum homomorphic encryption. IEEE Transactions on Information Forensics and Security, 18, 1145–1160.
71. Cheon, J. H., et al. (2017). Homomorphic encryption for arithmetic of approximate numbers. Advances in Cryptology – ASIACRYPT 2017, LNCS 10624, 409–437. Springer.
72. Diffie, W., & Hellman, M. E. (1976). New directions in cryptography. IEEE Transactions on Information Theory, 22(6), 644–654.
73. Islam, M. R., & Steinebach, M. (2022). AI-driven digital forensics for post-quantum encrypted traffic. Forensic Science International: Digital Investigation, 40, 301321.