李鱼,
李喆
武警工程大学密码工程学院 西安 710086
基金项目:国家自然科学基金(61572521)，武警工程大学科研创新团队科学基金(KYTD201805)，陕西省自然科学基础研究计划(2021-JM252)
详细信息
作者简介:韩益亮：男，1977年生，博士，教授，研究方向为信息安全、神经密码学
李鱼：男，1995年生，硕士生，研究方向为神经密码学
李喆：男，1994年生，硕士生，研究方向为神经密码学
通讯作者:韩益亮 hanyil@163.com
中图分类号:TN918; TP309.7计量
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被引次数:0
出版历程
收稿日期:2020-07-29
修回日期:2020-12-25
网络出版日期:2020-12-31
刊出日期:2021-08-10
A Key Exchange Optimization Scheme Based on Tree Parity Machine
Yiliang HAN,,Yu LI,
Zhe LI
Department of Cryptographic Engineering, Engineering University of PAP, Xi’an 710086, China
Funds:The National Natural Science Foundation of China (61572521), The Scientific Foundation of the Scientific Research and Innovation Team of Engineering University of PAP (KYTD201805), The Natural Science Basic Research Plan in Shaanxi Province (2021JM252)
摘要
摘要:树形奇偶机(TPM)之间的相互同步学习能够用于实现密钥交换方案，方案的安全性取决于树形奇偶机的结构参数。为了得到使得密钥交换方案安全性高且计算量小的参数，该文提出基于树形奇偶机的密钥交换优化方案。首先，定义向量化的学习规则，提高树形奇偶机同步学习的时间效率。其次，改进针对树形奇偶机同步学习的合作攻击算法，使其能够自适应参数的变化。最后，通过仿真实验对方案进行了效率和安全性测试。实验结果表明，树形奇偶机的向量化能使同步时间减少约90%，但不会减少同步所需的步数，即不影响方案的安全性。在可用于生成512 bit固定长度密钥的结构参数中，(14, 14, 2)被合作攻击攻破的概率为0%，所需同步时间较少。因此，所提密钥交换优化方案是安全高效的。
关键词:密码学/
密钥交换/
人工神经网络/
树形奇偶机/
相互学习
Abstract:Synchronization of Tree Parity Machines (TPM) by mutual learning can be used to achieve key exchange schemes. The security of the scheme depends on the structure parameters of TPM. In order to obtain the parameters that make the key exchange scheme more secure and less computation, a key exchange optimization scheme based on TPM is proposed. Firstly, the learning rules of vectorization are defined to improve the efficiency of synchronization of TPM. Secondly, the cooperating attack algorithm for synchronization of TPM is improved to make it adaptive to the change of parameters. Finally, the efficiency and security of the scheme are tested by simulation experiment. The simulation results show that the vectorization of TPM can reduce the synchronization time by about 90%, which does not reduce the number of steps required for synchronization and affect the security. Among the parameters that can be used to generate 512 bit fixed length key, the probability of (14, 14, 2) being attacked by cooperating attack is 0%, and the synchronization time is less. Therefore, the proposed key exchange optimization scheme is secure and efficient.
Key words:Cryptography/
Key exchange/
Artificial neural network/
Tree Parity Machine(TPM)/
Mutual learning
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