Fault attacks are among the most effective techniquesto break real implementations of cryptographic algorithms. They usually require some kind of knowledge bythe attacker on the effect of the faults on the target device, which in practice turns to be a poorly reliable informationtypically affected by uncertainty. This paper is devoted toaddress this problem by softening the a-priori knowledge on the injection technique needed by the attacker in the contextof Differential Fault Analysis (DFA). We conceive an originalsolution, named J-DFA, based on translating the stage ofdifferential cryptanalysis of DFA attacks into terms of fittingmultiple models to data corrupted by outliers. Specifically, wetailor J-Linkage algorithm [9] to the fault analysis. In order toshow the effectiveness of J-DFA and its benefits in practicalscenarios, we applied the technique under different attackconditions.

J-DFA: A novel approach for robust differential fault analysis

Magri L.;Fragneto P.;
2016-01-01

Abstract

Fault attacks are among the most effective techniquesto break real implementations of cryptographic algorithms. They usually require some kind of knowledge bythe attacker on the effect of the faults on the target device, which in practice turns to be a poorly reliable informationtypically affected by uncertainty. This paper is devoted toaddress this problem by softening the a-priori knowledge on the injection technique needed by the attacker in the contextof Differential Fault Analysis (DFA). We conceive an originalsolution, named J-DFA, based on translating the stage ofdifferential cryptanalysis of DFA attacks into terms of fittingmultiple models to data corrupted by outliers. Specifically, wetailor J-Linkage algorithm [9] to the fault analysis. In order toshow the effectiveness of J-DFA and its benefits in practicalscenarios, we applied the technique under different attackconditions.
2016
Proceedings - 2015 Workshop on Fault Diagnosis and Tolerance in Cryptography, FDTC 2015
978-1-4673-7579-5
AES
Fault attack
Fault model
J-Linkage
Robust clustering
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1188392
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact