Research Article
Multiple Differential Distinguisher of SIMECK32/64 Based on Deep Learning
Algorithm 3
Data generation for NDrm.
| | Input: | | | multiple differences (Δ0, Δ1, …, Δt − 1) | | | sample number N | | | Output: TD’’ | | (1) | TD″ ← (⋅)/∗initial data set∗/ | | (2) | K ← Random() | | (3) | for i = 0 to t − 1 do | | (4) | P2i = Random() | | (5) | end for | | (6) | for i = 0 to t − 1 do | | (7) | P2i + 1 = P2i ⊕ Δi | | (8) | end for | | (9) | for j = 0 to N − 1 do | | (10) | Cj ← encrypt (Pj, Kj) | | (11) | end for | | (12) | for i = 0 to N − 1 do/∗set label∗/ | | (13) | if i&1 = 0 then | | (14) | Ci ← Random() | | (15) | Yi ← 0 | | (16) | else | | (17) | Yi ← 1 | | (18) | end if | | (19) | end for | | (20) | return TD’’ ← (X(C0…CN − 1), Y) |
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