arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1901.02438cs.CRcs.LGcs.NE
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利用模糊位和神经网络部分反演几轮密码散列函数

Using fuzzy bits and neural networks to partially invert few rounds of some cryptographic hash functions

Oles Honchar Dnipro National University(奥列格·洪查尔第聂伯国立大学)

Sergij V. Goncharov

摘要:

我们考虑模糊位或连续位,它们取[0;1]和(-1 1]而不是{0;1}中的值,以及对它们的操作(NOT,XOR等)。并且在它们的序列(ADD)上,获得用于由模糊比特组成的消息的密码散列函数CHF的一般化,使得CHF成为消息的每个比特的平滑和非常数函数。然后,我们训练神经网络来预测具有给定散列的消息,其中预测消息的散列和给定真实散列的损失函数是反向传播的。给出并比较了标准CHF- MD5、SHA1、SHA 2 -256和SHA 3/SHA 256-的训练结果,其中包含少量(可选的弱化)轮次。

英文摘要:

We consider fuzzy, or continuous, bits, which take values in [0;1] and (-1;1] instead of {0;1}, and operations on them (NOT, XOR etc.) and on their sequences (ADD), to obtain the generalization of cryptographic hash functions, CHFs, for the messages consisting of fuzzy bits, so that CHFs become smooth and non-constant functions of each bit of the message. We then train the neural networks to predict the message that has a given hash, where the loss function for the hash of predicted message and given true hash is backpropagatable. The results of the trainings for the standard CHFs - MD5, SHA1, SHA2-256, and SHA3/Keccak - with small number of (optionally weakened) rounds are presented and compared.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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