Authors - Banda Rithija, MV Parth, Haripriya L, Skandan SS, Manju Abstract - The task of identifying Cryptographic Algorithms from ciphertext is a challenge within digital forensics and security auditing, when there is no knowledge of either the plaintext or the key used. As modern encryption algorithms increase in sophistication, their output becomes indistinguishable from random noise, rendering traditional pattern recognition techniques ineffective. This paper proposes a two-stage Hierarchical Cipher Classifiers, the first stage discriminates among three major Cryptographic Families: Symmetric, Asymmetric, and Hash; the second stage identifies the specific algorithm within those families in the context of six Modern Encryption Standards: Advanced Encryption Standard, Triple Data Encryption Standard, Blowfish, Rivest–Shamir–Adleman, ElGamal, and Secure Hash Algorithm 256-bit. In order to achieve high accuracy, we developed a hybrid feature space consisting of 167 attributes that included both Statistical and Transform- Domain Features.We incorporated SHapley Additive exPlanations (SHAP) into our classifiers to address the concern of the black-box nature of Deep Learning. Empirical Results indicate that the Hierarchical Classifier Structure has produced a substantial reduction in the rate of misclassifications compared to flat classifiers, offering a transparent and effective tool for automated cryptanalysis.
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