By Stefan Mangard, Axel Y. Poschmann

This booklet constitutes the completely refereed post-conference lawsuits of the sixth foreign Workshop, COSADE 2015, held in Berlin, Germany, in April 2015. The 17 revised complete papers offered have been rigorously chosen from forty eight submissions. the focal point 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.

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Extra info for Constructive Side-Channel Analysis and Secure Design: 6th International Workshop, COSADE 2015, Berlin, Germany, April 13-14, 2015. Revised Selected Papers

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Each neighbor is constructed by moving one of the windows left or right (if we see the projection vector as a row vector). The lengths of the moves considered are small multiples of the window length (as set by the num hops parameter). During the computation of the neighbors, the collisions between windows are avoided in order to keep d distinct windows. Next, the best neighbor is selected as the neighbor having the maximal evaluation of fobj on the set Lptr . This best neighbor is finally tested to detect if a d-tuple of shares is spanned by the windows.

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. 4. Success rate for NTA, ETA, SVM and RF in fct. of the number of useless samples u, for various sizes of the profiling set Np , with d = 2, SNR=1, Na = 15. Another noticeable element of Fig. 4 is that SVM and RF seem to be bounded to lower success rates than TA.

As far as the technical parameters are concerned, we first set the number of hops (num hops) in the find sol phase to allow the windows covering all the dimensions of the traces. It enables an iteration to find a covering set of windows when one window is incorrectly placed. Next, in the improve sol phase, the more move steps (move steps) and resize steps (resize steps), the quicker the algorithm converges towards the optimal windows, but the longer each iteration is. We found that a good tradeoff in our context was to use move steps of 1, Efficient Selection of Time Samples for Higher-Order DPA 43 Algorithm 4.

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