网络与信息安全学报 ›› 2023, Vol. 9 ›› Issue (2): 154-163.doi: 10.11959/j.issn.2096-109x.2023029
侯泽洲1,2, 任炯炯1,2, 陈少真1,2
修回日期:
2022-03-28
出版日期:
2023-04-25
发布日期:
2023-04-01
作者简介:
侯泽洲(1998- ),男,山西朔州人,信息工程大学硕士生,主要研究方向为基于深度学习的轻量级分组密码安全性分析基金资助:
Zezhou HOU1,2, Jiongjiong REN1,2, Shaozhen CHEN1,2
Revised:
2022-03-28
Online:
2023-04-25
Published:
2023-04-01
Supported by:
摘要:
神经网络区分器作为一种新的可被应用于密码算法安全性分析的工具,一经提出便被应用于多种密码算法的安全性分析。对于 SIMON-like 算法,其循环移位参数的选择有多种。利用神经网络区分器对分组长度为32 bit的SIMON-like算法的循环移位参数(a,b,c)的安全性进行了研究,并给出了好的循环移位参数选择。利用K?lbl等在CRYPTO2015中提出的SIMON-like算法仿射等价类思想,将分组长度为32 bit的SIMON-like算法的循环移位参数划分至509个等价类,并选择其中使gcd(a-b,2)=1成立的240个等价类进行研究。针对240个等价类的代表元构建了自动化搜索差分路径的SAT/SMT模型,并利用SAT/SMT求解器搜索了不同代表元的多轮最佳差分路径。利用搜索得到的最佳差分路径的输入差分训练了神经网络区分器,选择其中准确率最高的神经网络区分器作为代表元的神经网络区分器,统计了不同代表元的神经网络区分器准确率。发现K?lbl等给出的20个最佳循环参数并不能使神经网络区分器的准确率最低,而且其中4个循环移位参数对应的神经网络区分器的准确率超过了80%,这意味着这4个循环移位参数抗神经网络区分器的能力是差的。综合考虑K?lbl等的选择和不同代表元的神经网络区分器准确率,给出了3个好的循环移位参数选择,即(6,11,1)、(1,8,3)和(6,7,5)。
中图分类号:
侯泽洲, 任炯炯, 陈少真. 基于神经网络区分器的SIMON-like算法参数安全性评估[J]. 网络与信息安全学报, 2023, 9(2): 154-163.
Zezhou HOU, Jiongjiong REN, Shaozhen CHEN. Security evaluation for parameters of SIMON-like cipher based on neural network distinguisher[J]. Chinese Journal of Network and Information Security, 2023, 9(2): 154-163.
表5
等价类划分Table 5 Equivalent class partitioning"
类别 | 准确率Acc(a,b,c) | 等价类代表元(a,b,c) |
A | [0.5,0.6) | (10,15,3),(1,6,3),(3,12,7),(4,9,6),(1,12,3),(4,13,7),(5,12,6),(2,11,1),(5,14,1),(5,12,1),(1,12,5),(4,11,1),(7,12,3), (7,14,1),(7,12,6),(3,12,2),(3,14,5),(2,7,1),(4,13,1),(4,11,6),(4,15,3),(5,6,1),(1,12,2),(10,11,1),(11,12,1),(2,15,3), (6,7,1),(1,14,3), (14,15,9),(1,10,5),(4,7,2),(7,14,3),(7,10,1),(5,6,3),(12,13,1),(4,5,1),(2,3,5),(4,5,2), |
B | [0.6,0.7) | (4,7,1),(6,7,4),(10,13,2),(10,15,2), |
C | [0.7,0.8) | (4,15,1),(11,12,3),(8,9,4),(8,11,4),(1,8,4),(4,9,1),(12,15,1),(9,12,1),(6,9,4),(3,8,1),(8,9,3),(3,10,1),(3,8,4),(7,14,2), (1,8,9),(10,11,6),(0,3,4),(6,7,10),(1,6,4),(0,3,1),(10,15,6),(2,15,5),(2,7,4),(0,1,4),(5,14,2),(3,14,2),(2,3,1),(1,14,2), (14,15,2),(0,9,1),(8,9,1),(6,13,4),(2,9,8),(7,10,4),(1,10,0),(1,2,8),(5,6,4), |
D | [0.8,0.9) | (1,4,8),(4,7,0),(4,5,8), |
E | [0.9,1.0) | (1,6,8),(5,6,0),(2,3,8),(2,7,0),(1,6,6),(2,7,2),(5,6,6),(2,3,2),(2,7,7),(1,6,1),(1,14,1),(2,3,3),(2,9,2),(1,10,10),(4,7,7 (2,5,2),(1,12,1),(1,2,2),(1,4,1),(4,5,5),(2,9,9),(1,10,1),(2,5,5),(1,2,1),(3,12,4),(1,8,0),(1,12,4),(12,15,4),(12,13,4), (8,15,1),(8,9,0),(0,1,8),(7,8,9),(0,15,1),(1,10,8),(2,9,0),(2,5,8),(1,2,0),(4,7,4),(4,5,4),(1,4,4),(3,4,4),(1,8,1),(8,9,9), (0,1,1),(1,8,8),(8,9,8),(0,1,0) |
表6
K?lbl的20个最佳循环参数划分Table 6 K?lbl's 20 optimal parameters partitioning"
循环参数 | 准确率 | 所属类别 |
(0,1,2) | 0.816 2 | D |
(0,1,3) | 0.560 9 | A |
(1,2,3) | 0.577 9 | A |
(3,4,5) | 0.582 9 | A |
(0,5,10) | 0.816 2 | D |
(0,5,15) | 0.560 9 | A |
(4,5,3) | 0.607 0 | B |
(0,7,14) | 0.816 2 | D |
(6,7,5) | 0.559 4 | A |
(1,8,3) | 0.554 5 | A |
(3,8,14) | 0.797 78 | C |
(7,8,5) | 0.554 5 | A |
(5,10,15) | 0.577 9 | A |
(6,11,1) | 0.547 0 | A |
(1,12,7) | 0.582 9 | A |
(5,12,3) | 0.582 9 | A |
(7,12,1) | 0.587 4 | A |
(0,13,10) | 0.816 2 | D |
(0,13,7) | 0.560 9 | A |
(8,13,2) | 0.795 1 | C |
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