Machine Learning Meets Cryptanalysis: Exploring IND-CPA and IND-CCA Security
Testing how strong encryption really is is essential for security. Even when systems come with formal mathematical proofs, weak designs or misuse can still leak information. Machine learning is now used as a practical tool to automatically spot patterns and weaknesses. It does not replace mathematical proofs, but it helps confirm which systems are insecure and which ones remain strong in real-world testing.
What is Cryptanalysis?
Cryptanalysis is the study of breaking or weakening cryptographic systems. If cryptography builds the locks, cryptanalysis tries to pick them. Traditional methods include brute-force key search, differential cryptanalysis (studying how small changes in plaintext affect ciphertext), linear cryptanalysis (finding approximate linear relations between inputs and outputs), and side-channel attacks (exploiting timing or power leaks).
Cryptographers often model security using formalized ‘games’ as a reasoning framework. For example, in the IND-CPA (Indistinguishability under Chosen-Plaintext Attack) game, an attacker can encrypt any messages it chooses, and then must guess which of two challenge messages was encrypted. A stronger definition is IND-CCA (Indistinguishability under Chosen-Ciphertext Attack), where the attacker also has access to a decryption oracle.
Advanced Encryption Standard (AES) in Counter (CTR) mode with proper nonces is considered IND-CPA secure, while Rivest–Shamir–Adleman (RSA) with Optimal Asymmetric Encryption Padding (OAEP) achieves IND-CCA security [1]. By contrast, textbook RSA without padding (the plain deterministic form of RSA) or AES in Electronic Code Book (ECB) mode are insecure, because deterministic patterns leak information [2].
Machine Learning in Cryptanalysis
Machine learning (ML) offers a new lens for cryptanalysis. Instead of humans trying to spot biases by hand, neural networks can be trained on ciphertexts and learn subtle patterns automatically. So far, ML has been able to:
- Find new distinguishers for lightweight or reduced-round ciphers
- Strengthen classical methods like linear cryptanalysis by spotting patterns more efficiently
- Act as an 'auditor' that automatically detects when insecure schemes (like AES-ECB) leak patterns
Some recent studies show this in practice. Kim et al. [3] used classifiers to test indistinguishability: they easily detected weaknesses in AES-ECB and textbook RSA but failed to break strong schemes like AES-CTR or RSA-OAEP. Hou et al. [4] improved linear cryptanalysis of DES by letting ML highlight useful biases, extending attacks to more rounds. Dani et al. [5] tested lightweight ciphers like SPECK and SIMON, and ML models could do no better than random guessing on the full versions-though reduced-round variants showed slight leakage.
Together, these results illustrate the dual role of ML: it can quickly flag insecure designs, but it also confirms when modern ciphers remain strong.
Future Directions
ML is not a substitute for rigorous proofs of IND-CCA security, but it serves as a powerful practical auditor. Future research may explore reinforcement learning agents for adaptive chosen-ciphertext games, generative models for finding new distinguishers, and automated ML 'audits' for new ciphers. By combining formal proofs with empirical ML testing, we get both mathematical assurance and real-world confidence.
Edited By: Windhya Rankothge, PhD, Canadian Institute for Cybersecurity
References
1. Sibleyras, F., “Security of Modes of Operation and other provably secure cryptographic schemes” (Doctoral dissertation, Sorbonne Université). https://theses.hal.science/tel-03058306, 2020
2. Fujisaki, E., Okamoto, T., Pointcheval, D. and Stern, J., “RSA-OAEP is secure under the RSA assumption”. In Annual International Cryptology Conference (pp. 260-274). Berlin, Heidelberg: Springer Berlin Heidelberg. 2001 https://link.springer.com/article/10.1007/s00145-002-0204-y
3. Kim, B.D., Vasudevan, V.A., D'Oliveira, R.G., Cohen, A., Stahlbuhk, T. and Médard, M., 2025. “Cryptanalysis via machine learning based information theoretic metrics”. arXiv preprint arXiv:2501.15076. 2025, https://arxiv.org/abs/2501.15076
4. Hou, Z., Ren, J. and Chen, S., 2025. “Improved machine learning-aided linear cryptanalysis: application to DES”. Cybersecurity, 8(1), p.22. 2025. https://cybersecurity.springeropen.com/articles/10.1186/s42400-024-00327-4
5. Dani, J., Nakka, K. and Saxena, N., 2024. “A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers”. arXiv preprint arXiv:2405.19683. https://arxiv.org/abs/2405.19683