Designing Security Protocols Using Novel Neural Network Model
Tieming Chen, Rongrong Jiang · 2007
Two neural networks with the common input vector can finally synchronize their weight vectors by output- based mutual learning. It can be well utilized to negotiate secure information over a public channel. Designing security protocols based on such synchronized neural network model is quite advantageous for its low-cost and high-performance. In this paper, we at first analyze and optimize the interacting network neurl, then present a cryptography-oriented secure parity model and implement the performance simulations. As an instance, a novel key agreement protocol design scenario is finally proposed.