Cache-Collision Timing Attacks Against AES

Author(s):  
Joseph Bonneau ◽  
Ilya Mironov
Keyword(s):  
2010 ◽  
Vol 33 (7) ◽  
pp. 1153-1164
Author(s):  
Xin-Jie ZHAO ◽  
Tao WANG ◽  
Yuan-Yuan ZHENG
Keyword(s):  

Author(s):  
Johannes Mittmann ◽  
Werner Schindler

AbstractMontgomery’s and Barrett’s modular multiplication algorithms are widely used in modular exponentiation algorithms, e.g. to compute RSA or ECC operations. While Montgomery’s multiplication algorithm has been studied extensively in the literature and many side-channel attacks have been detected, to our best knowledge no thorough analysis exists for Barrett’s multiplication algorithm. This article closes this gap. For both Montgomery’s and Barrett’s multiplication algorithm, differences of the execution times are caused by conditional integer subtractions, so-called extra reductions. Barrett’s multiplication algorithm allows even two extra reductions, and this feature increases the mathematical difficulties significantly. We formulate and analyse a two-dimensional Markov process, from which we deduce relevant stochastic properties of Barrett’s multiplication algorithm within modular exponentiation algorithms. This allows to transfer the timing attacks and local timing attacks (where a second side-channel attack exhibits the execution times of the particular modular squarings and multiplications) on Montgomery’s multiplication algorithm to attacks on Barrett’s algorithm. However, there are also differences. Barrett’s multiplication algorithm requires additional attack substeps, and the attack efficiency is much more sensitive to variations of the parameters. We treat timing attacks on RSA with CRT, on RSA without CRT, and on Diffie–Hellman, as well as local timing attacks against these algorithms in the presence of basis blinding. Experiments confirm our theoretical results.


10.29007/nwj8 ◽  
2019 ◽  
Author(s):  
Sebastien Carré ◽  
Victor Dyseryn ◽  
Adrien Facon ◽  
Sylvain Guilley ◽  
Thomas Perianin

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.


Author(s):  
Mário S. Alvim ◽  
Konstantinos Chatzikokolakis ◽  
Annabelle McIver ◽  
Carroll Morgan ◽  
Catuscia Palamidessi ◽  
...  
Keyword(s):  

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