Spiking Neurons with ASNN Based-Methods for the Neural Block Cipher

TL;DR

This paper proposes a neural block cipher using Spiking Neural Networks (SNNs) that enhances security and speed in key generation and encryption, leveraging non-linearity and timing-based encoding for robust cryptographic applications.

Contribution

Introduces a novel neural block cipher utilizing SNNs for secure, fast key generation and encryption, with improved resistance to attacks compared to traditional methods.

Findings

SNN-based cipher offers low vulnerability to brute-force attacks.

The approach enables high-speed, network-level encryption.

The method improves security through non-linearity and timing encoding.

Abstract

Problem statement: This paper examines Artificial Spiking Neural Network (ASNN) which inter-connects group of artificial neurons that uses a mathematical model with the aid of block cipher. The aim of undertaken this research is to come up with a block cipher where by the keys are randomly generated by ASNN which can then have any variable block length. This will show the private key is kept and do not have to be exchange to the other side of the communication channel so it present a more secure procedure of key scheduling. The process enables for a faster change in encryption keys and a network level encryption to be implemented at a high speed without the headache of factorization. Approach: The block cipher is converted in public cryptosystem and had a low level of vulnerability to attack from brute, and moreover can able to defend against linear attacks since the Artificial Neural Networks (ANN) architecture convey non-linearity to the encryption/decryption procedures. Result: In this paper is present to use the Spiking Neural Networks (SNNs) with spiking neurons as its basic unit. The timing for the SNNs is considered and the output is encoded in 1's and 0's depending on the occurrence or not occurrence of spikes as well as the spiking neural networks use a sign function as activation function, and present the weights and the filter coefficients to be adjust, having more degrees of freedom than the classical neural networks. Conclusion: In conclusion therefore, encryption algorithm can be deployed in communication and security applications where data transfers are most crucial. So this paper, the neural block cipher proposed where the keys are generated by the SNN and the seed is considered the public key which generates the both keys on both sides In future therefore a new research will be conducted on the Spiking Neural Network (SNN) impacts on communication.