Neural-Network-Based Pseudo-Random Number Generator Evaluation Tool for Stream Ciphers
Neural-Network-Based Pseudo-Random Number Generator Evaluation Tool for Stream Ciphers
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DOI：
10.1109/candarw.2019.00065
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Hayato Kimura;Takanori Isobe;T. Ohigashi
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文献类型：
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作者：
Hayato Kimura;Takanori Isobe;T. Ohigashi
流密码的安全性取决于其伪随机数发生器（PRNG）的性质。虽然有一些方法可以评估PRNG，例如自动对随机数或特定密码分析相关搜索进行统计测试的工具，但这些方法需要有关统计偏差的先验知识，并且无法发现未知偏差。Hirose证明NIST SP 800-22（PRNG的统计测试套件）无法检测线性同余发生器（LCG）的线性;因此NIST SP 800-22忽略了统计偏差。我们提出了一个详尽的方法来搜索PRNG的统计偏差。所提出的方法可以自动发现未知类型的偏见，使用神经网络来检测目标PRNG的输出和理想的随机数之间的微小差异。我们将所提出的方法应用于RC 4流密码和LCG。结果表明，所提出的方法检测不同类型的统计偏差在两种不同的算法，而无需这些偏见的先验知识。具体地说，所提出的方法可以发现LCG的线性，不能被NIST SP 800-22检测。
The security of stream ciphers depends on the properties of their pseudo-random number generators (PRNGs). Although there are methods to evaluate PRNGs, such as tools that automatically conduct statistical tests for random numbers or specific cryptanalysis-related searches, these methods require prior knowledge about statistical biases and cannot find unknown biases. Hirose demonstrates that NIST SP 800-22 (a statistical test suite for PRNGs) cannot detect the linearity of a linear congruential generator (LCG); thus NIST SP 800-22 overlooks statistical biases. We propose an exhaustive method to search for statistical biases in PRNGs. The proposed method can automatically discover unknown types of biases using a neural network to detect slight differences between the target PRNG's output and ideal random numbers. We applied the proposed method to the RC4 stream cipher and LCG. The results demonstrate that the proposed method detects different types of statistical biases in two different algorithms without prior knowledge of these biases. Specifically, the proposed method could discover the linearity of LCG that cannot be detected by NIST SP 800-22.