  Many real-world cyber-physical systems (CPS) use proprietary cipher
algorithms. In this work, we describe an easy-to-use black-box security
evaluation approach to measure the strength of proprietary ciphers without
having to know the algorithms. We quantify the strength of a cipher by
measuring how difficult it is for a neural network to mimic the cipher
algorithm. We define new metrics (e.g., cipher match rate, training data
complexity and training time complexity) that are computed from neural networks
to quantitatively represent the cipher strength. This measurement approach
allows us to directly compare the security of ciphers. Our experimental
demonstration utilizes fully connected neural networks with multiple parallel
binary classifiers at the output layer. The results show that when compared
with round-reduced DES, the security strength of Hitag2 (a popular stream
cipher used in the keyless entry of modern cars) is weaker than 3-round DES.
