Entropy makes it possible to measure the uncertainty about an information source from the distribution of its output symbols. It is known that the maximum Shannon’s entropy of a discrete source of information is reached when its symbols follow a Uniform distribution. In cryptography, these sources have great applications since they allow for the highest security standards to be reached. In this work, the most effective estimator is selected to estimate entropy in short samples of bytes and bits with maximum entropy. For this, 18 estimators were compared. Results concerning the comparisons published in the literature between these estimators are discussed. The most suitable estimator is determined experimentally, based on its bias, the mean square error short samples of bytes and bits.

中文翻译：

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为具有最大熵的短位和字节序列选择有效的熵估计器


熵使得可以根据信息源的输出符号的分布来测量信息源的不确定性。众所周知，当离散信息源的符号服从均匀分布时，其香农熵达到最大。在密码学中，这些来源具有很大的应用，因为它们可以达到最高的安全标准。在这项工作中，选择最有效的估计器来估计具有最大熵的字节和比特的短样本中的熵。为此，对 18 名估算者进行了比较。讨论了文献中发表的这些估计器之间的比较结果。最合适的估计器是根据其偏差、字节和位的短样本的均方误差通过实验确定的。