Distinguishing Lightweight Block Ciphers in Encrypted Images

Girish Mishra; S. K. Pal; S. V. S. S. N. V. G. Krishna Murthy; Kanishk Vats; Rakshak Raina

doi:10.14429/dsj.71.16843

Girish Mishra DRDO-Scientific Analysis Group, Delhi - India https://orcid.org/0000-0002-7489-2721
S. K. Pal DRDO-Scientific Analysis Group, Delhi - India https://orcid.org/0000-0003-3297-1605
S. V. S. S. N. V. G. Krishna Murthy DRDO-Defence Institute of Advanced Technology, Pune - India https://orcid.org/0000-0002-4180-0253
Kanishk Vats Delhi Technological University, Delhi - India https://orcid.org/0000-0002-4260-7033
Rakshak Raina Bennett University, Greater Noida - India https://orcid.org/0000-0002-9763-2447

DOI: https://doi.org/10.14429/dsj.71.16843

Keywords: Deep learning, Cryptography, Cryptanalysis, Lightweight block ciphers, MNIST, Fashion-MNIST

Abstract

Modern day lightweight block ciphers provide powerful encryption methods for securing IoT communication data. Tiny digital devices exchange private data which the individual users might not be willing to get disclosed. On the other hand, the adversaries try their level best to capture this private data. The first step towards this is to identify the encryption scheme. This work is an effort to construct a distinguisher to identify the cipher used in encrypting the traffic data. We try to establish a deep learning based method to identify the encryption scheme used from a set of three lightweight block ciphers viz. LBlock, PRESENT and SPECK. We make use of images from MNIST and fashion MNIST data sets for establishing the cryptographic distinguisher. Our results show that the overall classification accuracy depends firstly on the type of key used in encryption and secondly on how frequently the pixel values change in original input image.