Abstract: Abstract Symmetric block ciphers, such as the Advanced Encryption Standard (AES), are deterministic algorithms which transform plaintexts to ciphertexts using a secret key. These ciphers are designed such that it is computationally very difficult to recover the secret key if only pairs of plaintexts and ciphertexts are provided to the attacker. Constraint solvers have recently been suggested as a way of recovering the secret keys of symmetric block ciphers. To carry out such an attack, the attacker provides the solver with a set of equations describing the mathematical relationship between a known plaintext and a known ciphertext, and then attempts to solve for the unknown secret key. This approach is known to be intractable against AES unless side-channel data -- information leaked from the cryptographic device due to its internal physical structure -- is introduced into the equation set.
A significant challenge in writing equations representing side-channel data is measurement noise. In this work we show how casting the problem as a pseudo-Boolean optimization instance provides an efficient and effective way of tolerating this noise. We describe a theoretical analysis, connecting the measurement signal-to-noise ratio and the tolerable set size of a non-optimizing solver with the success probability. We then conduct an extensive performance evaluation, comparing two optimizing variants for dealing with measurement noise to a non-optimizing method. Our best optimizing method provides a successful attack on the AES cipher which requires surprisingly little side-channel data and works in reasonable computation time. We also make available a set of AES cryptanalysis instances and provide some practical feedback on our experience of using open-source constraint solvers.