Abstract
Neural cryptography is based on synchronization of tree parity machines by mutuallearning. We extend previous key-exchange protocols by replacing random inputs withqueries depending on the current state of the neural networks. The probability of asuccessful attack is calculated for different model parameters using numerical simulations.The results show that queries restore the security against cooperating attackers. Thesuccess probability can be reduced without increasing the average synchronization time.
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