Ciphertext-only classification of encryption algorithms using deep learning
Year: 2026
Type: article
Abstract: This study presents a machine learning approach for identifying encryption algorithms based solely on ciphertext analysis. The problem is formulated as a supervised classification task, where features are learned directly from ciphertext samples generated by four widely used symmetric encryption algorithms. Experimental results show that the model can reliably distinguish between ciphers under constrained conditions, particularly when initialization vectors (IVs) and key distributions remain consistent across samples. These findings indicate that ciphertexts retain subtle statistical patterns that can be exploited for algorithm identification, while also highlighting limitations in generalization under distribution shifts.
Source: Journal of Intelligent Systems
Institutions: Satbayev University, Al-Farabi Kazakh National University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,695
OpenAlex ID: W7212013066