AI-Based Cryptography: A Comprehensive Review of Adaptive Encryption, Neural Cryptanalysis, and Post-Quantum Resilience
Received: 25 November 2025 | Revised: 28 January 2026 | Accepted: 14 February 2026 | Online: 4 April 2026
Corresponding author: Mohammed A. Abdewi
Abstract
Nowadays, protecting information is paramount. With the rapid evolution of cyber threats and the growing potential of quantum computing to compromise classical encryption methods, the integration of Artificial Intelligence (AI) into cryptography has become a promising area for exploration. This review outlines recent advances in AI-based encryption techniques, such as dynamic key generation, adversarial neural cryptography, and AI-enhanced cryptanalysis. It compares AI-based approaches to traditional cryptographic systems in terms of adaptability, security strength, and quantum resistance. This paper also summarizes key research contributions and highlights the benefits and current challenges of deploying AI-based cryptography, including computational overhead and the absence of formal security guarantees. The review aims to offer an introductory overview of how AI is reshaping the future of encryption and to identify areas that require further research and validation.
Keywords:
artificial intelligence, AI-based encryption, cryptography, quantum resistance, adversarial neural networks, dynamic key generation, adaptive encryptionDownloads
References
T. Bakhshi and B. Ghita, "Anomaly Detection in Encrypted Internet Traffic Using Hybrid Deep Learning," Security and Communication Networks, vol. 2021, no. 1, 2021, Art. no. 5363750. DOI: https://doi.org/10.1155/2021/5363750
R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing, "CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy," in Proceedings of The 33rd International Conference on Machine Learning, June 2016, pp. 201–210.
S. S. Chaeikar, A. Jolfaei, and N. Mohammad, "AI-Enabled Cryptographic Key Management Model for Secure Communications in the Internet of Vehicles," IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 4589–4598, Apr. 2023. DOI: https://doi.org/10.1109/TITS.2022.3200250
D. Xu, G. Li, W. Xu, and C. Wei, "Design of artificial intelligence image encryption algorithm based on hyperchaos," Ain Shams Engineering Journal, vol. 14, no. 3, Apr. 2023, Art. no. 101891. DOI: https://doi.org/10.1016/j.asej.2022.101891
I. Meraouche, S. Dutta, H. Tan, and K. Sakurai, "Learning asymmetric encryption using adversarial neural networks," Engineering Applications of Artificial Intelligence, vol. 123, Aug. 2023, Art. no. 106220. DOI: https://doi.org/10.1016/j.engappai.2023.106220
P. Singh, S. Dutta, and P. Pranav, "Optimizing GANs for Cryptography: The Role and Impact of Activation Functions in Neural Layers Assessing the Cryptographic Strength," Applied Sciences, vol. 14, no. 6, Mar. 2024. DOI: https://doi.org/10.3390/app14062379
T. Sowmya and E. A. M. Anita, "A comprehensive review of AI based intrusion detection system," Measurement: Sensors, vol. 28, Aug. 2023, Art. no. 100827. DOI: https://doi.org/10.1016/j.measen.2023.100827
M. Abadi and D. G. Andersen, "Learning to Protect Communications with Adversarial Neural Cryptography." arXiv, Oct. 21, 2016.
M. Stypiński and M. Niemiec, "Synchronization of Tree Parity Machines Using Nonbinary Input Vectors," IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 1, pp. 1423–1429, Jan. 2024. DOI: https://doi.org/10.1109/TNNLS.2022.3180197
A. Gohr, "Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning," in Advances in Cryptology – CRYPTO 2019, 2019, pp. 150–179. DOI: https://doi.org/10.1007/978-3-030-26951-7_6
K. D. Pandl, S. Thiebes, M. Schmidt-Kraepelin, and A. Sunyaev, "On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda," IEEE Access, vol. 8, pp. 57075–57095, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2981447
A. Benamira, D. Gerault, T. Peyrin, and Q. Q. Tan, "A Deeper Look at Machine Learning-Based Cryptanalysis," in Advances in Cryptology – EUROCRYPT 2021, 2021, pp. 805–835. DOI: https://doi.org/10.1007/978-3-030-77870-5_28
I. Amir, H. Suhaimi, R. Mohamad, E. Abdullah, and C. H. Pu, "Hybrid encryption based on a generative adversarial network," Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, no. 2, Aug. 2024, Art. no. 971. DOI: https://doi.org/10.11591/ijeecs.v35.i2.pp971-978
J. W. Lee et al., "Privacy-Preserving Machine Learning With Fully Homomorphic Encryption for Deep Neural Network," IEEE Access, vol. 10, pp. 30039–30054, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3159694
A. A. Hussain and F. Al‐Turjman, "Artificial intelligence and blockchain: A review," Transactions on Emerging Telecommunications Technologies, vol. 32, no. 9, Sept. 2021, Art. no. e4268. DOI: https://doi.org/10.1002/ett.4268
A. Sarkar, "A symmetric neural cryptographic key generation scheme for Iot security," Applied Intelligence, vol. 53, no. 8, pp. 9344–9367, Apr. 2023. DOI: https://doi.org/10.1007/s10489-022-03904-7
R. T. Elmaghraby, N. M. Abdel Aziem, M. A. Sobh, and A. M. Bahaa-Eldin, "Encrypted network traffic classification based on machine learning," Ain Shams Engineering Journal, vol. 15, no. 2, Feb. 2024, Art. no. 102361. DOI: https://doi.org/10.1016/j.asej.2023.102361
N. Kshetri, M. M. Rahman, M. M. Rana, O. F. Osama, and J. Hutson, "algoTRIC: Symmetric and Asymmetric Encryption Algorithms for Cryptography – A Comparative Analysis in AI Era," International Journal of Advanced Computer Science and Applications, vol. 15, no. 12, 2024. DOI: https://doi.org/10.14569/IJACSA.2024.0151201
S. Pramanik et al., "A Novel Approach Using Steganography and Cryptography in Business Intelligence:," in Advances in Business Information Systems and Analytics, A. Azevedo and M. F. Santos, Eds. IGI Global, 2021, pp. 192–217. DOI: https://doi.org/10.4018/978-1-7998-5781-5.ch010
D. Dai and S. Boroomand, "A Review of Artificial Intelligence to Enhance the Security of Big Data Systems: State-of-Art, Methodologies, Applications, and Challenges," Archives of Computational Methods in Engineering, vol. 29, no. 2, pp. 1291–1309, Mar. 2022. DOI: https://doi.org/10.1007/s11831-021-09628-0
M. N. Al-Suqri and M. Gillani, "A Comparative Analysis of Information and Artificial Intelligence Toward National Security," IEEE Access, vol. 10, pp. 64420–64434, 2022. DOI: https://doi.org/10.1109/ACCESS.2022.3183642
A. A. Pise et al., "Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues," Computational Intelligence and Neuroscience, vol. 2022, pp. 1–14, Aug. 2022. DOI: https://doi.org/10.1155/2022/8421434
S. B. Hegde, S. Srivastav, and N. B. Ks, "A Comparative study on state of art Cryptographic key distribution with quantum networks," in 2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT), Oct. 2022, pp. 1–7. DOI: https://doi.org/10.1109/GCAT55367.2022.9971870
H. Taherdoost, T. V. Le, and K. Slimani, "Cryptographic Techniques in Artificial Intelligence Security: A Bibliometric Review," Cryptography, vol. 9, no. 1, Mar. 2025, Art. no. 17. DOI: https://doi.org/10.3390/cryptography9010017
K. Kumar, S. Tanwar, and S. Kumar, "Deep-Learning-based Cryptanalysis through Topic Modeling," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12524–12529, Feb. 2024. DOI: https://doi.org/10.48084/etasr.6515
A. Saini and R. Sehrawat, "An intelligent and efficient CNN-AES framework for image block encryption with a multi-key approach," Engineering Research Express, vol. 7, no. 1, Mar. 2025, Art. no. 015206. DOI: https://doi.org/10.1088/2631-8695/ada3af
U. Rawat, Abhishek, H. Singh, and A. Ur Rehman, "Cybersecurity Challenges and Risks in AGI Development and Deployment," in Artificial General Intelligence (AGI) Security, S. El Hajjami, K. Kaushik, and I. U. Khan, Eds. Springer Nature Singapore, 2025, pp. 291–314. DOI: https://doi.org/10.1007/978-981-97-3222-7_14
Downloads
How to Cite
License
Copyright (c) 2026 Mohammed A. Abdewi, Farouk A. Emara, Ashraf A. Gouda, Mohammed A. Razek
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
