By Stefan Mangard, Axel Y. Poschmann
ISBN-10: 3319214756
ISBN-13: 9783319214757
ISBN-10: 3319214764
ISBN-13: 9783319214764
This ebook constitutes the completely refereed post-conference lawsuits of the sixth overseas Workshop, COSADE 2015, held in Berlin, Germany, in April 2015. The 17 revised complete papers provided have been conscientiously chosen from forty eight submissions. the point of interest of this workshop used to be on following subject matters: side-channel assaults, FPGA countermeasures, timing assaults and countermeasures, fault assaults, countermeasures, and Hands-on Side-channel analysis.
Read or Download Constructive Side-Channel Analysis and Secure Design: 6th International Workshop, COSADE 2015, Berlin, Germany, April 13-14, 2015. Revised Selected Papers PDF
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Additional resources for Constructive Side-Channel Analysis and Secure Design: 6th International Workshop, COSADE 2015, Berlin, Germany, April 13-14, 2015. Revised Selected Papers
Example text
26 L. Lerman et al. Plaintext xor Sbox ... Sbox deterministic part Key noise Sbox 0 ... 0 addition noise addition deterministic part noise addition Sample 1 addition noise Sample d Sample d+1 Sample d+u Fig. 2. Simulated leaking implementations. 6 Evaluation Metrics The efficiency of side-channel attacks can be quantified according to various metrics. We will use information theoretic and security metrics advocated in [24]. Success Rate (SR). g. a key byte) and allowing to sort the different candidates, we define the success rate of order o as the probability that the correct subkey is ranked among the first o candidates.
The evaluator perfectly knows the leakages’ PDF). In this context, we will use Pr for both Prmodel and Prchip (since they are equal) and omit subscripts for the leakages l to lighten notations. Proposition 1. ). Then the mutual information leakage MI(K; X, L) estimated with their (perfect) leakage models is the same. Proof. As clear from the definitions in Sect. 6, the mutual/perceived information estimated thanks to TA only depend on Pr[k|l]. So we need to show that these conditional probabilities Pr[k|l2 ] and Pr[k|l1 ] are equal.
Lerman et al. interesting observation relates to RF in high dimensionality, which remarkably resists the addition of useless samples (compared to SVM and TA). The main reason for this behavior is the random feature selection embedded into this tool. That is, for a sufficient number of trees, RF eventually detects the informative POI in the traces, which makes it less sensitive to the increase of u. By contrast, TA and SVM face a more and more difficult estimation problem in this case. 0 0 50 100 150 200 250 number of non−informative points Fig.