AI-ASSISTED POST-QUANTUM CRYPTOGRAPHY FOR BLOCKCHAIN AND IOT SYSTEMS: EMERGING THREATS, INTELLIGENT DEFENSES, AND QUANTUM-SAFE ARCHITECTURES
Keywords:
Post-Quantum Cryptography, Artificial Intelligence, Blockchain Security, Internet of Things (IoT), Quantum-Safe Architecture, CryptanalysisAbstract
The rapid advancement of quantum computing and artificial intelligence (AI) is fundamentally transforming the cybersecurity landscape, creating unprecedented opportunities as well as critical threats to modern cryptographic infrastructures. Conventional cryptographic algorithms such as Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC), and Diffie–Hellman are increasingly vulnerable to quantum-enabled attacks, particularly through Shor’s and Grover’s algorithms, which threaten the confidentiality, integrity, and authenticity of digital communication systems. Simultaneously, the proliferation of blockchain technologies and Internet of Things (IoT) ecosystems has expanded the attack surface of cyber-physical environments, necessitating intelligent and quantum-resistant security mechanisms. This review article comprehensively investigates the convergence of AI-assisted post-quantum cryptography (PQC), blockchain security, and IoT protection frameworks within emerging quantum-safe architectures. The paper critically analyzes the evolution of classical and post-quantum cryptographic algorithms, including lattice-based, code-based, hash-based, multivariate, and isogeny-based schemes standardized by the National Institute of Standards and Technology (NIST). Furthermore, the study explores the role of AI and machine learning in cryptanalysis, anomaly detection, adaptive authentication, automated threat intelligence, and intelligent defense orchestration. Special emphasis is placed on lightweight PQC techniques suitable for resource-constrained IoT environments and quantum-resistant blockchain infrastructures employing zero-knowledge proofs and decentralized trust mechanisms. The article also identifies major research gaps, scalability challenges, interoperability limitations, privacy concerns, and future opportunities associated with intelligent quantum-safe ecosystems. Finally, the review presents future research directions involving 6G security, federated learning, homomorphic encryption, quantum internet architectures, and self-adaptive cryptographic systems for next-generation cybersecurity resilience.
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References
1. Shor PW. Algorithms for quantum computation: discrete logarithms and factoring. Proc 35th Annu Symp Found Comput Sci. 1994:124–134.
2. Grover LK. A fast quantum mechanical algorithm for database search. Proc 28th Annu ACM Symp Theory Comput. 1996:212–219.
3. National Institute of Standards and Technology (NIST). Post-Quantum Cryptography Standardization Project. 2024. https://csrc.nist.gov/projects/post-quantum-cryptography
4. Bernstein DJ, Buchmann J, Dahmen E. Post-Quantum Cryptography. Springer; 2009.
5. Chen L, Jordan S, Liu YK, Moody D, Peralta R, Perlner R, et al. Report on Post-Quantum Cryptography. NISTIR 8105. 2016.
6. Katz J, Lindell Y. Introduction to Modern Cryptography. 3rd ed. CRC Press; 2020.
7. Stallings W. Cryptography and Network Security. 8th ed. Pearson; 2022.
8. Paar C, Pelzl J. Understanding Cryptography. Springer; 2010.
9. Diffie W, Hellman M. New directions in cryptography. IEEE Trans Inf Theory. 1976;22(6):644–654.
10. Rivest RL, Shamir A, Adleman L. A method for obtaining digital signatures and public-key cryptosystems. Commun ACM. 1978;21(2):120–126.
11. National Institute of Standards and Technology. FIPS 197: Advanced Encryption Standard (AES). 2001.
12. National Institute of Standards and Technology. Secure Hash Standard (SHA-2). FIPS 180-4. 2015.
13. Merkle RC. A certified digital signature. Adv Cryptology—CRYPTO. 1989:218–238.
14. Boneh D, Shoup V. A Graduate Course in Applied Cryptography. 2020.
15. Katz J, Yung M. Threshold cryptosystems. Adv Cryptology. 2001.
16. Menezes AJ, van Oorschot PC, Vanstone SA. Handbook of Applied Cryptography. CRC Press; 1996.
17. Koblitz N. Elliptic curve cryptosystems. Math Comput. 1987;48(177):203–209.
18. Miller VS. Use of elliptic curves in cryptography. Adv Cryptology—CRYPTO. 1985:417–426.
19. National Security Agency. SHA-3 Competition Report. 2012.
20. Nakamoto S. Bitcoin: A peer-to-peer electronic cash system. 2008.
21. Wood G. Ethereum Yellow Paper. 2014.
22. Zohar A. Bitcoin: under the hood. Commun ACM. 2015;58(9):104–113.
23. Antonopoulos AM. Mastering Bitcoin. O’Reilly Media; 2017.
24. Christidis K, Devetsikiotis M. Blockchains and smart contracts. IEEE Access. 2016;4:2292–2303.
25. Tapscott D, Tapscott A. Blockchain Revolution. Penguin; 2016.
26. Atzori M. Blockchain technology and decentralized governance. 2017.
27. Zheng Z, Xie S, Dai H, Chen X, Wang H. Blockchain challenges and opportunities. Int J Web Grid Serv. 2017.
28. Dai W. Crypto++ library documentation. 2020.
29. Liskov M, Rivest RL. Public key cryptography standards. 2018.
30. Bernstein DJ. Introduction to post-quantum cryptography. 2017.
31. Alagic G et al. Status report on PQC. NIST; 2022.
32. Regev O. On lattices, learning with errors, and cryptography. J ACM. 2009.
33. Peikert C. A decade of lattice cryptography. Found Trends Theor Comput Sci. 2016.
34. McEliece RJ. A public-key cryptosystem based on algebraic coding theory. 1978.
35. Berlekamp E, McEliece R, van Tilborg H. On the inherent intractability of certain coding problems. IEEE Trans Inf Theory. 1978.
36. Damgård I. A design principle for hash functions. 1989.
37. Buchmann J, Ding J. Post-Quantum Cryptography. Springer; 2009.
38. Patarin J. Hidden field equations. 1996.
39. De Feo L. Mathematics of isogeny-based cryptography. 2017.
40. Jao D, De Feo L. Towards quantum-resistant cryptosystems. 2011.
41. Costello C, Longa P, Naehrig M. Efficient algorithms for post-quantum cryptography. 2016.
42. Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press; 2016.
43. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–444.
44. Bishop CM. Pattern Recognition and Machine Learning. Springer; 2006.
45. Russell S, Norvig P. Artificial Intelligence: A Modern Approach. Pearson; 2020.
46. Sommer R, Paxson V. Outside the closed world: On using ML for security. IEEE S&P. 2010.
47. Moustafa N et al. ML-based intrusion detection systems. 2021.
48. Shone N, Ngoc TN, Phai VD, Shi Q. A deep learning approach for IDS. 2018.
49. Kim G, Lee S, Kim S. A novel hybrid IDS using deep learning. 2020.
50. He K, Zhang X, Ren S, Sun J. Deep residual learning. 2016.
51. Zhang Y, Yang Q. Federated learning. 2018.
52. McMahan B et al. Communication-efficient learning. 2017.
53. Kairouz P et al. Advances and open problems in federated learning. 2021.
54. Papernot N et al. Practical black-box attacks. 2017.
55. Szegedy C et al. Intriguing properties of neural networks. 2014.
56. Carlini N, Wagner D. Towards evaluating robustness of neural networks. 2017.
57. Biggio B, Roli F. Wild patterns: Ten years after. 2018.
58. Goodfellow I et al. Generative adversarial nets. 2014.
59. Mirsky Y et al. Kitsune: An ensemble IDS. 2018.
60. Liu Y, Peng J, et al. AI in cybersecurity survey. 2023.
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