Abstract
Cryptographic algorithms classification, which can detect the underlying encryption algorithm on sufficient large ciphertexts, is essential to encrypted traffic analysis and protocol compliance detection. Previous studies have typically employed various feature quantities and models for feature learning in analyzing encryption algorithms. Unlike these, this work performs a broader feature selection and extracts features from the P-values of the randomness test and their data distributions for different block cipher algorithms. This work utilizes the LightGBM framework to focus on block cipher algorithms classification in ECB mode. It takes six algorithms to test the classification scheme, including AES-128, AES-192, AES-256, DES, 3DES and SM4, with an average accuracy of 82%. To compare the accuracy, this work analyzes the influence weights of random features and experiments with the classification accuracy of different schemes on the same ciphertext blocks. The experiment results show that our scheme is effective in classifying block ciphers.
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This research was supported by the Key Research and Development Program Project of Shandong Province under grants No. 2020CXGC010115.
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Liu, S., Luo, M., Peng, C., He, D. (2023). Block Ciphers Classification Based on Randomness Test Statistic Value via LightGBM. In: Wang, D., Yung, M., Liu, Z., Chen, X. (eds) Information and Communications Security. ICICS 2023. Lecture Notes in Computer Science, vol 14252. Springer, Singapore. https://doi.org/10.1007/978-981-99-7356-9_3
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DOI: https://doi.org/10.1007/978-981-99-7356-9_3
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