AI-Assisted Cryptanalysis

Overview

This research project explores whether machine learning techniques can assist in cryptanalysis and what those results imply for modern and post-quantum cryptography. While artificial intelligence has strong pattern recognition capabilities, modern cryptographic systems are intentionally designed to remove the types of statistical patterns that AI models rely on.

To demonstrate this concept, our team implemented a machine learning model trained to attack a classical Vigenère cipher. The purpose was not to break modern encryption directly, but to show how AI can exploit statistical structure in simpler systems and use those findings to better understand the limits of AI in more advanced cryptographic environments.

Research Question

Can machine learning models assist in cryptanalysis by identifying statistical patterns in encrypted data, and how do those capabilities translate to modern cryptography and post-quantum encryption systems?

Methodology

To demonstrate machine learning-based cryptanalysis, we built a small-scale simulation using the classical Vigenère cipher. This cipher was selected because it exposes frequency-based patterns that can potentially be learned by machine learning models.

• Random encryption keys were generated for each experiment.
• Ciphertext datasets of 1,000, 5,000, and 20,000 samples were created.
• Key lengths of 3, 4, and 6 were tested to observe how cipher complexity impacts model performance.
• The model was trained to identify statistical patterns within ciphertext and predict likely key values.

Results

Machine learning models successfully identified statistical patterns in encrypted data when those patterns existed. Model performance improved as dataset size increased.

However, accuracy declined as encryption complexity increased. When testing longer keys, prediction accuracy dropped to approximately 17–21% for key length six.

Limitations

• The experiment used a simplified classical cipher.
• Training data consisted of synthetic ciphertext.
• Dataset size was limited to 20,000 samples.

Security Implications

AI is unlikely to directly break modern encryption algorithms like AES or RSA because these systems are designed to eliminate statistical patterns.

However, machine learning may significantly enhance side-channel attacks by detecting patterns in power consumption, timing behavior, or electromagnetic emissions that may reveal secret keys.