End-to-end automated cache-timing attack driven by Machine Learning

16 pages•Published: September 6, 2019

Sebastien Carré, Victor Dyseryn, Adrien Facon, Sylvain Guilley and Thomas Perianin

Abstract

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.

Keyphrases: cache timing attack, deep learning, ecdsa, flush+flush, machine learning, openssl

In: Karine Heydemann, Ulrich Kühne and Letitia Li (editors). Proceedings of 8th International Workshop on Security Proofs for Embedded Systems, vol 11, pages 1-16.

Links:	https://easychair.org/publications/paper/jZH5
	https://doi.org/10.29007/nwj8

BibTeX entry

@inproceedings{PROOFS2019:End_end_automated_cache,
  author    = {Sebastien Carré and Victor Dyseryn and Adrien Facon and Sylvain Guilley and Thomas Perianin},
  title     = {End-to-end automated cache-timing attack driven by Machine Learning},
  booktitle = {Proceedings of 8th International Workshop on Security Proofs for Embedded Systems},
  editor    = {Karine Heydemann and Ulrich Kühne and Letitia Li},
  series    = {Kalpa Publications in Computing},
  volume    = {11},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/jZH5},
  doi       = {10.29007/nwj8},
  pages     = {1-16},
  year      = {2019}}

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