CRYPTO-MINE: Cryptanalysis via Mutual Information Neural Estimation
This work addresses cryptanalysis for security researchers, though it appears incremental as it applies existing neural MI estimation methods to cryptography.
The paper tackles the challenge of estimating mutual information between plaintext and ciphertext in cryptosystems using neural networks, finding that their approach can detect information leakage in multiple encryption schemes and novel network coding-based systems.
The use of Mutual Information (MI) as a measure to evaluate the efficiency of cryptosystems has an extensive history. However, estimating MI between unknown random variables in a high-dimensional space is challenging. Recent advances in machine learning have enabled progress in estimating MI using neural networks. This work presents a novel application of MI estimation in the field of cryptography. We propose applying this methodology directly to estimate the MI between plaintext and ciphertext in a chosen plaintext attack. The leaked information, if any, from the encryption could potentially be exploited by adversaries to compromise the computational security of the cryptosystem. We evaluate the efficiency of our approach by empirically analyzing multiple encryption schemes and baseline approaches. Furthermore, we extend the analysis to novel network coding-based cryptosystems that provide individual secrecy and study the relationship between information leakage and input distribution.