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Aspects of Neural Cryptography

Artificial Intelligence for Encryption and Cryptanalysis

A friend asked me about artificial intelligence in cryptography so I thought I would look into it. This is not a comprehensive overview, rather you are reading an article that is on the surface as an introduction to the topic based on curiosity.

  1. Stochastic
  2. Artificial neural network (ANN)
  3. Encryption


Given some encrypted data (“ciphertext”), the goal of the cryptanalyst is to gain as much information as possible about the original, unencrypted data (“plaintext”).

It is useful to consider two aspects of achieving this.

  1. The second is solving the key that is unique for a particular encrypted message or group of messages.


This is simply a fancy way to say random. The word meant random. Stochastic refers to a randomly determined process. The word first appeared in English to describe a mathematical object called a stochastic process, but now in mathematics the terms stochastic process and random process are considered interchangeable.

Artificial neural network (ANN)

Artificial neural networks or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules.


In cryptography, encryption is the process of encoding a message or information in such a way that only authorized parties can access it and those who are not authorized cannot. Encryption does not itself prevent interference, but denies the intelligible content to a would-be interceptor.

Written by

AI Policy and Ethics at Student at University of Copenhagen MSc in Social Data Science. All views are my own.

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