Cache timing attacks are serious security threats that exploit cache memories to steal secret information.We believe that the identification of a sequence of operations from a set of cache-timing data measurements is not a trivial step when building an attack. We present a recurrent neural network model able to automatically retrieve a sequence of function calls from cache-timings. Inspired from natural language processing, our model is able to learn on partially labelled data. We use the model to unfold an end-to-end automated attack on OpenSSL ECDSA on the secp256k1 curve. Contrary to most research, we did not need human processing of the traces to retrieve relevant information.
Timing Attack Analysis on AA<sub>β</sub> Cryptosystem
