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https://doi.org/10.1007/978-3-...
Part of book or chapter of book . 2004 . Peer-reviewed
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A Collision-Attack on AES

Authors: Gregor Leander; Patrick Felke; Christof Paar; Kai Schramm;

A Collision-Attack on AES

Abstract

Recently a new class of collision attacks which was originally suggested by Hans Dobbertin has been introduced. These attacks use side channel analysis to detect internal collisions and are generally not restricted to a particular cryptographic algorithm. As an example, a collision attack against DES was proposed which combines internal collisions with side channel information leakage. It had not been obvious, however, how this attack applies to non-Feistel ciphers with bijective S-boxes such as the Advanced Encryption Standard (AES). This contribution takes the same basic ideas and develops new optimized attacks against AES. Our major finding is that the new combined analytical and side channel approach reduces the attack effort compared to all other known side channel attacks. We develop several versions and refinements of the attack. First we show that key dependent collisions can be caused in the output bytes of the mix column transformation in the first round. By taking advantage of the birthday paradox, it is possible to cause a collision in an output with as little as 20 measurements. If a SPA leak is present from which collisions can be determined with certainty, then each collision will reveal at least 8 bits of the secret key. Furthermore, in an optimized attack, it is possible to cause collisions in all four output bytes of the mix column transformation with an average of only 31 measurements, which results in knowledge of all 32 key bits. Finally, if collisions are caused in all four columns of the AES in parallel, it is possible to determine the entire 128-bit key with only 40 measurements, which a is a distinct improvement compared to DPA and other side channel attacks.

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    83
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
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    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
83
Top 10%
Top 1%
Top 10%
bronze