Advanced cyber security using Spectral entity feature selection based on Cyber Crypto Proof Security Protocol (C2PSP)

The growth of internet become more development in communication medium to provide various services. Information sharing ad security is mostly suffered by crime attackers because of different models of cyber-attacks are carried out by attackers. Attackers creates jamming principles, communication delays, packet dropping, and information hacking, duplicate injection to do so many activities to destroy the security. Based on the communication data analysis and features are non-identified and difficult to find the malicious activities. So the development of cyber security needs advancement to find the attackers based on the communication breaking activities. To resolve this problem, we propose a Spectral entity feature selection based Cyber Crypto Proof Security Protocol (C2PSP) to improve the cyber security. The Defect Scaling Rate (DSR) is used to estimate the communication defect rate. By marginalize the scaling rate using Spectral entity feature selection approach (SEFSA) is applied to select the features and trained to identify with Artificial neural network classifier (ANN). Based on the attack principles and activities in communication medium, the Cyber Crypto Proof Security Protocol (C2PSP) is applied to ensure the security verification and validation to process the data safer and securely. The proposed system produce high performance compared to other system as well to identify the malicious activities to improve the security against the cyber-attacks