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The best example of black-box, end-to-end learning of the type you describe in the literature is probably Greydanus' work on Learning the Enigma With Recurrent Neural Networks. They achieve functional key recovery for the restricted version of Enigma they study, but require much more data and computing power than traditional cryptanalysis of the same ...
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The $48$-bit state $s_i$ evolves per a Linear Congruential Random Number Generator, with modulus $n=2^{48}$, multiplier $a=\mathtt{5DEECE66D}_{16}$, and additive constant $b=\mathtt B_{16}$; that is $s_i=(a\cdot s_{i-1}+b)\bmod2^{48}$. We are given the OR $r_i$ of its two upper bits for $i\in[1,81]$, or equivalently the $81$ booleans $s_i\ge2^{46}$. Our goal ...
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The authors state in 4.3 that the design of the cipher has the following clever feature:
The dependencies between the LFSRs and the keystream vary greatly with the initial value of $R_4$ and since the variables of $R_4$ play no role in the value of the keystream, so it is not possible to find them.
So, later on they suggest the values of $R_4$ must be ...
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This is the pre-image attack on hash functions, i.e.
pre-image attack : given a hash value $y$ find a pre-image $x$ such that $y=h(x)$.
Good designed cryptographic hash functions have $\mathcal{O}(2^{n})$ classical pre-image resistance for $n$-bit output. The classical attack tries all possible input space to find a $x$ that hashes the target hash value. ...
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For weak ciphers, sure, for somewhat modern ciphers, e.g enigma, short of possible but not as efficient as other methods. for modern cryptography? No
Machine Learning is a very broad field so obviously I can't give conclusive statements about what can't be done. But in general if we think of gradient decent techniques we need a notion of getting close to a ...
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