# AI Makes Crypto Evolve
## 6 Aiac
### Neural Hashing  
Recurrent Neural Networks (RNNs) [111], Cell Neural Network [112].  
In an article [111], the hash value--a fixed-size value-- was returned by the cryptographic Hash function. RNN as a possible approach was validated by a software implementation of RNN. The principles of RNN and possibilities of RNN as well as experiment-tested RNNs with three layers on the basis of several examples of general texts were presented. An efficient one-way hash function is a crucial part of modern cryptography research.  
A hash function construction based on a cell neural network was proposed with the chaotic sequence generated by a cell neural network using the Runge-Kutta algorithm [112]. Through the transformation of the latter chaos sequence, the hash code was obtained and could be generated from the former hash result.  
Previous generations of Hashing algorithms were often deployed in combination with chaotic functions to avoid prediction. However, they revealed vulnerabilities because of the involvement of AI. Combining AI with chaotic functions could introduce more secure hashes and offer resistance to ML attacks.  
The most commonly used method is to combine ANNs with hash techniques to ensure greater security. Secure Hash Algorithms (SHA), as the standard of cryptography Hash Functions, helps to provide message integrity, authentication and digital signature. Due to the properties of Chaos and NNs, a new technology based on chaotic NNs was used [113]. By conducting a comparison of these two methods, a new structure of Hash function was presented.  
There are also studies that focus on the efficiency of the hashing algorithm. The NNs that were used aimed to speed up the program. The effective generation of the Hash function is a transformation that takes an input and returns a fixed-size value representing the achievement of security in today's networks. The ANN was used for the hash function generation and provided network configuration [114]. Moreover, due to the compact and efficient binary codes of image Hashing, it is often applied to large-scale content-based visual retrieval.  
Neural Networks can also be combined with the chaotic function to produce secure hashes. The chaotic function and NNs were used in data encryption due to their cipher-suitable properties, and a hash function based on them was constructed which made use of their advantageous properties. The proposed function [115] encodes the plaintext of arbitrary length into the fixed length, improving security against statistical attacks, birthday attacks, and meet-in-the-middle attacks. Furthermore, the implemented Hash function demonstrated good statistical properties, strong Collision Resistance and High MessageSensitivity. Furthermore, this proposed algorithm [116] can be used in cryptography, which includes the following two major operations: generation of NN parameters using fast and efficient Chaotic Generator and the iteration of the message through the Chaotic NN. Effective hash functions are the cornerstone of security in today's communication networks. A similar idea was presented in [117], which focused on a theoretical analysis of the possibility of using ANNs and chaotic maps for hashing. ANN could be used to generate a one way hash function. This paper used several testing sets to show the validity of the presented proposal. Similar uses of chaotic maps were apparent in [118]. It proposed a new structure named Keyed Sponge Chaotic Neural Network (KSCNN hash function) based on chaotic maps, NN and sponge construction. The security of the proposed hash function improved after KSCNN. The theoretical analysis and experimental simulations showed that the proposed hash function KSCNN has good statistical properties and strong collision resistance.