基于深度学习的随机性检验策略研究

doi:10.11959/j.issn.1000-436x.2023111

Abstract

Abstract:

In order to achieve better test performance, researches on the randomness test strategies based on deep learning were conducted, including the batch average strategy proposed by EUROCRYPT 2021 and the selection strategy for data unit size.By introducing the randomness statistical test model based on deep learning methods, the statistical distribution and test power expression of two test strategies were theoretically derived, and it was pointed out that: (i) the batch average strategy could amplify the prediction accuracy of the model, but it was prone to an increase in the probability of the second type of error in statistics, instead reducing the statistical test power; (ii) the smaller data units of the deep model generally obtained the more powerful statistical tests.Based on the above understanding, a new bit-level deep learning model was proposed for randomness statistical tests, which gained the advantage of prediction with 80 times fewer parameters and 50% samples, compared with the previous work on linear congruent generator (LCG) algorithm, and achieved significant prediction advantages with 10～20 times fewer parameters by extending the model to apply to 5～7 rounds of Speck, compared with the model proposed by Gohr.

Key words: deep learning, randomness, statistical test, random number generator, Speck, LCG, batch average strategy

CLC Number:

TN918.1

Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG. Research on test strategy for randomness based on deep learning[J]. Journal on Communications, 2023, 44(6): 23-33.

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References 20

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检验项目			p
检验项目		5轮	6轮	7轮
SP800-2	频数	0.000 000	0.000 000	0.237 403
	块内频数	0.000 000	0.000 000	0.679 483
	游程	0.000 000	0.000 000	0.856 241
	最长1游程	0.000 000	0.806 792	0.465 166
	矩阵秩	0.000 000	0.346 675	0.644 905
	离散傅里叶	0.000 000	0.000 000	0.183 176
	非重叠模板匹配	0.000 000	0.000 000	0.945 836
	重叠模板匹配	0.000 000	0.452 246	0.956 599
	通用统计	0.000 000	0.000 136	0.618 025
	线性复杂度	0.639 372	0.026 867	0.464 382
	重叠子序列	0.000 000	0.042 517	0.954 304
	近似熵	0.000 000	0.000 000	0.981 640
	累加和	0.000 000	0.000 000	0.416 601
	随机游动	0.000 000	0.000 000	0.255 535
	随机游动变量	0.000 000	0.000 000	0.564 618
Gohr分类准确率		0.929 513	0.788 817	0.612 240

Research on test strategy for randomness based on deep learning

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