Entropy Analysis of Cryptographic Keys Using Machine Learning

The strength of cryptography key is the basic ingredient of safe information systems, and it directs the strength of encryption algorithms towards attacks. Evaluation methods that rely on entropy measures alone, e.g. Shannon entropy or min-entropy, show any signs of subtle structure patterns or biases in weak keys. In this paper, a machine learning-based framework of a thorough analysis and classification of cryptographic keys are proposed. The model combines entropy values with statistical characteristics-such as variance, runs, autocorrelation-as well as assessment of various supervised learning models, such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector machines (SVM) and Random Forest classifier. Experiments performed on a synthetic dataset of 10,000 keys (128-bits and 256 bits) indicate that ensemble-based models especially Random Forest have better accuracy of more than 99 percent, demonstrate stability in a noisy environment as well as generalization between various sizes of keys. Further tests, such as ablation tests, cross-validation sensibility tests, and tests of importance of features prove the paramount importance of integrating entropy and statistical features in order to do successful key classification. The offered solution has been suggested as a scalable, automated, and trustworthy method of cryptographic key strength analysis, and its possible usage is related to security-sensitive systems and mechanisms of key generators.