Cooperating Attackers in Neural Cryptography

TL;DR

This paper introduces the Majority-Flipping Attacker, a cooperative attack strategy in neural cryptography that remains effective regardless of model parameters, challenging previous assumptions of security.

Contribution

It presents the first cooperative attack method in neural cryptography with analytical backing, demonstrating its consistent success across different model configurations.

Findings

The Majority-Flipping Attacker outperforms previous attacks in success rate.

Cooperation among attackers enhances attack effectiveness.

Analytical results align with simulation data.

Abstract

A new and successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The advanced attacker presented here, named the ``Majority-Flipping Attacker'', is the first whose success does not decay with the parameters of the model. This new attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.