A Review on Biological Inspired Computation in Cryptology

Cryptology is a field that concerned with cryptography and cryptanalysis. Cryptography, which is a key technology in providing a secure transmission of information, is a study of designing strong cryptographic algorithms, while cryptanalysis is a study of breaking the cipher. Recently biological approaches provide inspiration in solving problems from various fields. This paper reviews major works in the application of biological inspired computational (BIC) paradigm in cryptology. The paper focuses on three BIC approaches, namely, genetic algorithm (GA), artificial neural network (ANN) and artificial immune system (AIS). The findings show that the research on applications of biological approaches in cryptology is minimal as compared to other fields. To date only ANN and GA have been used in cryptanalysis and design of cryptographic primitives and protocols. Based on similarities that AIS has with ANN and GA, this paper provides insights for potential application of AIS in cryptology for further research

Enhance the performance of Chaotic Generator in the Filed of Cryptography: A Secret Key Generation Approach

The main focus of this research paper is to propose and improvement of the data security using encryption and decryption method in ANN based chaotic generator of original value. The Binary value sequence of ASCII CODE is converted with two initial parameter, and converted value is again decrypted with same initial parameter. In which consists of Binary value of ASCII Code, chaotic neural network algorithm was used for encryption and decryption and it generates the chaotic sequence of random value for each A to Z letter. The generated random value is the encrypted binary ASCII values of A to Z sequence of original ASCII Code binary value, with same initial parameter. For simulation MATLAB software is used. This paper also includes improved experimental results and complete demonstration that ANN Based Chaotic Generator is successfully perform the cryptography

Cryptography using Artificial Neural Networks

A Neural Network is a machine that is designed to model the way in which the brain performs a task or function of interest. It has the ability to perform complex computations with ease. The objective of this project was to investigate the use of ANNs in various kinds of digital circuits as well as in the field of Cryptography. During our project, we have studied different neural network architectures and training algorithms. A comparative study is done between different neural network architectures for an Adder and their merits/demerits are discussed. Using a Jordan (Recurrent network), trained by back-propagation algorithm, a finite state sequential machine was successfully implemented. The sequential machine thus obtained was used for encryption with the starting key being the key for decryption process. Cryptography was also achieved by a chaotic neural network having its weights given by a chaotic sequence

Iot Based Alzheimer’s Disease Diagnosis Model for Providing Security Using Light Weight Hybrid Cryptography

Security in the Internet of things (IoT) is a broad yet active research area that focuses on securing the sensitive data being circulated in the network. The data involved in the IoT network comes from various organizations, hospitals, etc., that require a higher range of security from attacks and breaches. The common solution for security attacks is using traditional cryptographic algorithms that can protect the content through encryption and decryption operations. The existing solutions are suffering from major drawbacks, including computational complexities, time and space complexities, slower encryption, etc. Therefore, to overcome such drawbacks, this paper introduces an efficient light weight cryptographic mechanism to secure the images of Alzheimer’s disease (AD) being transmitted in the network. The mechanism involves major stages such as edge detection, key generation, encryption, and decryption. In the case of edge detection, the edge maps are detected using the Prewitt edge detection technique. Then the hybrid elliptic curve cryptography (HECC) algorithm is proposed to encrypt and secure the images being transmitted in the network. For encryption, the HECC algorithm combines blowfish with the elliptic curve algorithm to attain a higher range of security. Another significant advantage of the proposed method is selecting the ideal private key, which is achieved using the enhanced seagull optimization (ESO) algorithm. The proposed work has been tested in the Python tool, and the performance is evaluated with the Alzheimer’s dataset, and the outcomes proved its efficacy over the compared methods