中文摘要：

利用离散的神经网络模型构建密码协议是信息安全领域一项新的研究内容．首先介绍了一个基于神经网络的树型奇偶机模型，在综述基于树型奇偶机的神经密码协议研究基础上，分析了神经密码协议的权值同步方案存在模型稳定性和同步判定安全性问题，分别提出在激活函数中增加阈值和利用hash函数判定同步权值的改进方法，并利用方差分析和统计实验给出仿真结果，证实了神经密码协议的可行性，最后讨论了协议模型的安全攻击问题．

英文摘要：

Research on discretized-neural-network-based cryptographic protocols is a novel topic in information security domain. At the beginning of this paper, literature on such neural cryptography is surveyed, and then an interacting neural network model, named tree parity machine （TPM）, is introduced with a survey of its theoretical research. Then, several important issues regarding TPM＇s applications for cryptography are addressed and analyzed through engineering empirical study. Two key problems, namely, the stability of Weight synchronization and the security of synchronization, are investigated in detail. Two new concept methods, the threshold in neuron activation function and hash-based synchronization check, are proposed to improve the TPM-based cryptography application scheme. ANOVA and statistical experiments results show that using threshold can achieve faster and more stable weight synchronization, while using hash function can check weight synchronization precisely. Finally, attack analysis for the improved TPM-used protocol models is discussed.