  In this research, we introduce MIND-Crypt, a novel attack framework that uses
deep learning (DL) and transfer learning (TL) to challenge the
indistinguishability of block ciphers, specifically SPECK32/64 encryption
algorithm in CBC mode (Cipher Block Chaining) against Known Plaintext Attacks
(KPA). Our methodology includes training a DL model with ciphertexts of two
messages encrypted using the same key. The selected messages have the same
byte-length and differ by only one bit at the binary level. This DL model
employs a residual network architecture. For the TL, we use the trained DL
model as a feature extractor, and these features are then used to train a
shallow machine learning, such as XGBoost. This dual strategy aims to
distinguish ciphertexts of two encrypted messages, addressing traditional
cryptanalysis challenges.
  Our findings demonstrate that the DL model achieves an accuracy of
approximately 99% under consistent cryptographic conditions (Same Key or
Rounds) with the SPECK32/64 cipher. However, performance degrades to random
guessing levels (50%) when tested with ciphertext generated from different keys
or different encryption rounds of SPECK32/64. To enhance the results, the DL
model requires retraining with different keys or encryption rounds using larger
datasets (10^7 samples). To overcome this limitation, we implement TL,
achieving an accuracy of about 53% with just 10,000 samples, which is better
than random guessing. Further training with 580,000 samples increases accuracy
to nearly 99%, showing a substantial reduction in data requirements by over
94%. This shows that an attacker can utilize machine learning models to break
indistinguishability by accessing pairs of plaintexts and their corresponding
ciphertexts encrypted with the same key, without directly interacting with the
communicating parties.
