Abstract
This work proposes an improved second-order correlation power analysis attack based on a new refined expecter (\(\mathcal {RE}\)). The predicted \(\mathcal {RE}\) with the correct secret key is related to the Hamming weight of the Sbox output mask with a correlation coefficient of 0.35. It gives an improved attack performance in comparison with a traditional second-order attack which exhibits a correlation value of 0.24. In order to verify the practicability and performance of the proposed attack, we perform experiments on both simulated data and an AES implementation on an ARM SecureCore device, protected with first-order masking and shuffling countermeasures. The results demonstrate that our proposed attack outperforms the conventional second-order attack.
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Notes
- 1.
An signal-to-noise ratio (SNR) is the ratio between variance of signal and of noise, and denoted by \(\frac{\sigma ^{2}(signal)}{\sigma ^{2}(noise)}\) [12]. The higher SNR, the higher quality of the trace.
- 2.
This follows normal distribution with \(\mu =0\) and \(\sigma ^{2}\), where \(\mu \) and \(\sigma ^{2}\) indicate mean and variance, respectively.
- 3.
The guessing entropy indicates the average of how many key bytes remain to be guessed [9].
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Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2013R1A1A2A10062137). The authors would like to thank Dooho Choi at ETRI for supporting us with SCARF boards (http://www.k-scarf.or.kr/). The SCARF boards were supported by the KLA-SCARF project, the ICT R&D program of ETRI.
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A Performance Evaluation of Reasonable Hamming Weight Estimator Functions
A Performance Evaluation of Reasonable Hamming Weight Estimator Functions
Both reasonable Hamming weight estimator functions, Cho’s method [2] and Linge’s method [11], effectively calibrate the Hamming weight value in noise-free signals. On the contrary, in noisy environments, they are likely to have different performances. This section shows the performance evaluation of both in various SNR scenarios.
In this experiment, we consider simulation traces manipulating 8-bit random mask generation. For the sake of comparison, 6 different simulation traces of varying SNR were generated for exploitation in this experiment. Each type includes different white gaussian noise which is generated in MATLAB (simulator is described in Sect. 5.1 in detail). In this experiment, for each SNR, we run 50 experiments of 40, 000 simulation traces i.e. in total \(40,000\times 50=2,000,000\) traces are used for a certain SNR.
Table 3 shows that the SNR is proportional to success rate. White indicates that success rate of Linge’s method is higher than Cho’s, while dark gray in contrast to white. For both methods, the closer to 0 or 8 the Hamming weight, the lower the success rate. However, Linge’s method outperforms Cho’s for most Hamming weights except for 3–5, while the total resolution of Cho’s scheme is higher than the other on noisier traces.
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Ahn, H., Hanley, N., O’Neill, M., Han, DG. (2016). An Improved Second-Order Power Analysis Attack Based on a New Refined Expecter. In: Kim, Hw., Choi, D. (eds) Information Security Applications. WISA 2015. Lecture Notes in Computer Science(), vol 9503. Springer, Cham. https://doi.org/10.1007/978-3-319-31875-2_15
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