A Fussy Based Neural Genetic Algorithm for Securing Data in Cyber Security

Abstract

Businesses are using cyber security technologies more and more to upgrade their operations. These businesses are prone to hazards and cyber security breaches because to the very specialized characteristics of such settings, including their sensitive exchange of cyber security data and the weak design of connected devices. Our main goal is to develop a cyber security system that can take into account all potential forms of assaults while staying within the allocated budget. To achieve this, a financial strategy based on portfolio management is utilized by enabling the selection of a portfolio of security controls that maximizes security level control while minimizing direct expenses. To solve this problem we proposed Fussy Based Neural Genetic Algorithm for authenticity, reliability and confidentiality of cyber security data and it decreases the danger of cyber security data integrity. Using a complex key, the plaintext is first transformed into a complex cipher text. The key is created using logical operators and is randomly chosen from the cyber security data. By applying principles of proposed algorithm, the cipher text acquired in the first step is rendered even more unreadable in the second phase. Feature Extraction of cyber security data is done by Principle Component Analysis (PCA).The data is encrypted by using Data Encryption Standard (DES). The data is decrypted using the proposed Fussy Based Neural Genetic Algorithm with Particle Swarm Optimization (FNGA-PSO).The suggested model's metrics are examined and compared to various traditional algorithms. This model solves the lack of difference in the authenticity of cyber security information, as well as it will give real and effective information to the organizational companies.