Algebraic Side-Channel Analysis in the Presence of Errors

  • Yossef Oren
  • Mario Kirschbaum
  • Thomas Popp
  • Avishai Wool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6225)

Abstract

Measurement errors make power analysis attacks difficult to mount when only a single power trace is available: the statistical methods that make DPA attacks so successful are not applicable since they require many (typically thousands) of traces. Recently it was suggested by [18] to use algebraic methods for the single-trace scenario, converting the key recovery problem into a Boolean satisfiability (SAT) problem, then using a SAT solver. However, this approach is extremely sensitive to noise (allowing an error rate of well under 1% at most), and the question of its practicality remained open. In this work we show how a single-trace side-channel analysis problem can be transformed into a pseudo-Boolean optimization (PBOPT) problem, which takes errors into consideration. The PBOPT instance can then be solved using a suitable optimization problem solver. The PBOPT syntax provides for a more expressive input specification which allows a very natural representation of measurement errors. Most importantly, we show that using our approach we are able to mount successful and efficient single-trace attacks even in the presence of realistic error rates of 10%–20%. We call our new attack methodology Tolerant Algebraic Side-Channel Analysis (TASCA). We show practical attacks on two real ciphers: Keeloq and AES.

Keywords

Algebraic attacks power analysis side-channel attacks  pseudo-Boolean optimization 

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yossef Oren
    • 1
  • Mario Kirschbaum
    • 2
  • Thomas Popp
    • 2
  • Avishai Wool
    • 1
  1. 1.Computer and Network Security Lab, School of Electrical EngineeringTel-Aviv UniversityRamat AvivIsrael
  2. 2.Institute for Applied Information Processing and CommunicationsGraz University Of TechnologyAustria

Personalised recommendations