Neural Synchronization based Secret Key Exchange over Public Channels: A survey
Sandip Chakraborty, Jiban Dalal, Bikramjit Sarkar, Debaprasad, Mukherjee

TL;DR
This survey reviews neural synchronization-based secret key exchange methods over public channels, analyzing their security, attack vulnerabilities, and potential improvements for cryptographic applications.
Contribution
It provides a comprehensive overview of neural synchronization protocols, their security challenges, and discusses future directions for enhancing cryptographic robustness.
Findings
Deterministic algorithms have high time complexity for synchronization.
Probabilistic algorithms can decode keys during exchange.
Tree Parity Machine models are among the most secure.
Abstract
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…
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Taxonomy
TopicsChaos-based Image/Signal Encryption · Neural Networks and Applications · Cellular Automata and Applications
