One to One Identification of Cryptosystem Using Fisher’s Discriminant Analysis

DOI

10.2991/ijndc.2018.6.3.4How to use a DOI?

Keywords

Fisher’s Discriminant Analysis; One to One Identification; Cryptosystem; Block Cipher; Stream Cipher; Feature Extraction

Abstract

Distiguishing analysis is an important part of cryptanalysis. It is an important content of discriminating analysis that how to identify ciphertext is encrypted by which cryptosystems when it knows only ciphertext. In this paper, Fisher’s discriminant analysis (FDA), which is based on statistical method and machine learning, is used to identify 4 stream ciphers and 7 block ciphers one to one by extracting 9 different features. The results show that the accuracy rate of the FDA can reach 80% when identifying files that are encrypted by the stream cipher and the block cipher in ECB mode respectively, and files encrypted by the block cipher in ECB mode and CBC mode respectively. The average one to one identification accuracy rates of stream ciphers RC4, Grain, Sosemanuk are more than 55%. The maximum accuracy rate can reach 60% when identifying SMS4 from block ciphers in CBC mode one to one. The identification accuracy rate of entropy-based features is apparently higher than the probability-based features.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Xinyi Hu
AU  - Yaqun Zhao
PY  - 2018
DA  - 2018/07/31
TI  - One to One Identification of Cryptosystem Using Fisher’s Discriminant Analysis
JO  - International Journal of Networked and Distributed Computing
SP  - 155
EP  - 173
VL  - 6
IS  - 3
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2018.6.3.4
DO  - 10.2991/ijndc.2018.6.3.4
ID  - Hu2018
ER  -