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arXiv 1502.05153cs.CR
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基于神经同步的公共信道秘密密钥交换研究综述

Neural Synchronization based Secret Key Exchange over Public Channels: A survey

Sandip Chakraborty, Jiban Dalal, Bikramjit Sarkar, Debaprasad Mukherjee

摘要:

使用各种学习规则基于神经同步在公共信道上交换秘密密钥为基于数论的密码算法提供了一种有吸引力的替代方案。虽然有几种形式的攻击是可能的,例如几何,遗传和多数攻击,我们的调查发现,确定性算法,与端点网络同步具有高时间复杂度,而概率和基于人口的算法已被证明有能力解码的关键在其交换过程中的公共信道。我们的调查还讨论了查询,密码学,错误的信息,组密钥交换,突触深度等,已被提出,以增加时间复杂度的算法拦截或解码的关键在交换过程中。树奇偶校验机及其变种,神经网络与树拓扑结构,包括奇偶校验的状态位,似乎是最安全和稳定的端点网络模型之一。我们的调查还提到了一些值得注意的研究神经网络应用于其他必要的方面的密码学。我们的结论是,发现具有非常高的同步速度的神经架构,并设计在相互学习过程中交换的信息的编码和熵,以及设计非常敏感的混沌映射的网络的同步状态的混沌加密密钥的转换,是在这个领域的主要问题。

英文摘要:

Exchange of secret keys over public channels based on neural synchronization using a variety of learning rules offer an appealing alternative to number theory based cryptography algorithms. Though several forms of attacks are possible on this neural protocol e.g. geometric, genetic and majority attacks, our survey finds that deterministic algorithms that synchronize with the end-point networks have high time complexity, while probabilistic and population-based algorithms have demonstrated ability to decode the key during its exchange over the public channels. Our survey also discusses queries, heuristics, erroneous information, group key exchange, synaptic depths, etc, that have been proposed to increase the time complexity of algorithmic interception or decoding of the key during exchange. The Tree Parity Machine and its variants, neural networks with tree topologies incorporating parity checking of state bits, appear to be one of the most secure and stable models of the end-point networks. Our survey also mentions some noteworthy studies on neural networks applied to other necessary aspects of cryptography. We conclude that discovery of neural architectures with very high synchronization speed, and designing the encoding and entropy of the information exchanged during mutual learning, and design of extremely sensitive chaotic maps for transformation of synchronized states of the networks to chaotic encryption keys, are the primary issues in this field.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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