对基于深度学习的密钥恢复攻击的分析与改进

在 2019 年美密会议上, Gohr 提出了第一个基于深度学习的密钥恢复攻击, 并应用于 11 轮、12 轮 Speck32/64. 本文从时间复杂度的角度对该攻击进行分析和改进. 发现 Gohr 所提攻击的运行时间主要受解密、访问神经区分器、通过贝叶斯优化推荐密钥等三个操作的影响, 后两个操作几乎占据了全部运行时间; Gohr 采用的强化学习机制导致错误密文结构占据了过多计算资源. 提出了以下改进: (1) 攻击只采用在部分密文比特上建立的神经区分器, 并用查找表代替神经区分器, 使得攻击运行时可以完全摆脱对神经网络的依赖. (2) 放弃强化学习机制, 使用新的 ``Guess-and-F...

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Published inJournal of Cryptologic Research Vol. 10; no. 1; p. 168
Main Authors CHEN, Yi, Yan-Tian, SHEN, Hong-Bo, YU, 陈怡, 申焱天, 于红波
Format Journal Article
LanguageChinese
Published Beijing Chinese Association for Cryptologic Research, Journal of Cryptologic Research 01.02.2023
Subjects
Algorithms
Bayesian analysis
Complexity
Deep learning
Encryption
Lookup tables
Optimization
Recovery
Online AccessGet full text
ISSN2097-4116
DOI10.13868/j.cnki.jcr.000587

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Abstract 在 2019 年美密会议上, Gohr 提出了第一个基于深度学习的密钥恢复攻击, 并应用于 11 轮、12 轮 Speck32/64. 本文从时间复杂度的角度对该攻击进行分析和改进. 发现 Gohr 所提攻击的运行时间主要受解密、访问神经区分器、通过贝叶斯优化推荐密钥等三个操作的影响, 后两个操作几乎占据了全部运行时间; Gohr 采用的强化学习机制导致错误密文结构占据了过多计算资源. 提出了以下改进: (1) 攻击只采用在部分密文比特上建立的神经区分器, 并用查找表代替神经区分器, 使得攻击运行时可以完全摆脱对神经网络的依赖. (2) 放弃强化学习机制, 使用新的 ``Guess-and-Filter'' 策略. 通过贝叶斯优化推荐部分密钥的思想和 ``Guess-and-Filter'' 策略有冲突, 所以也放弃使用贝叶斯优化. 基于上述改进, 提出了新的密钥恢复攻击, 使得时间复杂度显著降低. 为了验证新的密钥恢复攻击在时间复杂度上的优势, 在11 轮、12 轮 Speck32/64 上进行了实际密钥恢复攻击, 时间复杂度分别为 226.68 和 232.25. 与已有的最优攻击相比, 复杂度分别减少为原来的 1/211.32 和 1/211.1. 此前没有研究从运行时间角度分析对基于深度学习的密钥恢复攻击, 本文工作有助于推动基于深度学习的密码分析的研究.
AbstractList 在 2019 年美密会议上, Gohr 提出了第一个基于深度学习的密钥恢复攻击, 并应用于 11 轮、12 轮 Speck32/64. 本文从时间复杂度的角度对该攻击进行分析和改进. 发现 Gohr 所提攻击的运行时间主要受解密、访问神经区分器、通过贝叶斯优化推荐密钥等三个操作的影响, 后两个操作几乎占据了全部运行时间; Gohr 采用的强化学习机制导致错误密文结构占据了过多计算资源. 提出了以下改进: (1) 攻击只采用在部分密文比特上建立的神经区分器, 并用查找表代替神经区分器, 使得攻击运行时可以完全摆脱对神经网络的依赖. (2) 放弃强化学习机制, 使用新的 ``Guess-and-Filter'' 策略. 通过贝叶斯优化推荐部分密钥的思想和 ``Guess-and-Filter'' 策略有冲突, 所以也放弃使用贝叶斯优化. 基于上述改进, 提出了新的密钥恢复攻击, 使得时间复杂度显著降低. 为了验证新的密钥恢复攻击在时间复杂度上的优势, 在11 轮、12 轮 Speck32/64 上进行了实际密钥恢复攻击, 时间复杂度分别为 226.68 和 232.25. 与已有的最优攻击相比, 复杂度分别减少为原来的 1/211.32 和 1/211.1. 此前没有研究从运行时间角度分析对基于深度学习的密钥恢复攻击, 本文工作有助于推动基于深度学习的密码分析的研究.
Author 于红波
Yan-Tian, SHEN
陈怡
申焱天
Hong-Bo, YU
CHEN, Yi
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SubjectTerms Algorithms
Bayesian analysis
Complexity
Deep learning
Encryption
Lookup tables
Optimization
Recovery
Title 对基于深度学习的密钥恢复攻击的分析与改进
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