Crucial pitfall of DPA Contest V4.2 implementation

Abstract

Differential power analysis (DPA) is a powerful side‐channel key recovery attack that efficiently breaks cryptographic algorithm implementations. In order to prevent these types of attacks, hardware designers and software programmers make use of masking and hiding techniques. DPA contest is an international framework that allows researchers to compare their power analysis attacks under the same conditions. The latest version of DPA contest, denoted as V4.2, provides an improved implementation of the rotating S‐box masking scheme where low‐entropy boolean masking is combined with the shuffling technique to protect Advanced Encryption Standard implementation on a smart card. The improvements were designed based on the awareness of implementation lacks analyzed from attacks carried out during the previous DPA contest V4. Therefore, this new approach is devised to resist most of the proposed attacks to the original rotating S‐box masking implementation. In this paper, we investigate the security of this new implementation in practice. Our analysis, focused on exploiting the first‐order leakage, discovered important lacks. The main vulnerability observed is that an adversary can mount a standard DPA attack aimed at the S‐box output in order to recover the whole secret key even when a shuffling technique is used. We tested this observation on a public dataset and implemented a successful attack that revealed the secret key using only 35 power traces. Copyright © 2017 John Wiley & Sons, Ltd.

Gap between Prediction and Truth: A Case Study of False-Positives in Leakage Detection

“…DPA contest v4.2 uses a rotating SBOX masking to protect the AES implementation on a smart card and collects 5,000 traces in each zip file [20]. is study decompresses the first zip file, corresponding to the SHA1sums: f711206b413b8d02f595d5861996ff61a1711f3d, to build DILSET_3.…”

Power Side-Channel Attack Analysis: A Review of 20 Years of Study for the Layman

“…In fact, machine learning represents a large area of growth in modern SCA, and has become the primary focus for modern profiling techniques. As machine learning is beyond the scope of this work, we would direct the interested reader to the following works: [57][58][59][60][61][62][63][64][65][66][67][68][69].…”