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

Abstract

The growth of the internet has become more developed in communication mediums to provide various services. Crime attackers predominantly suffer from information sharing ad security because attackers carry out different models of cyber-attacks. Attackers create jamming principles, communication delays, packet dropping, and information hacking, duplicate injection to do so many activities to destroy security. Based on the communication data analysis, and features are non-identified, and challenging to find malicious activities. So the development of cyber security needs advancement to find the attackers based on communication-breaking activities. To resolve this problem, we propose a Spectral entity feature selection based Cyber Crypto Proof Security Protocol (C2PSP) to improve cyber security. The Defect Scaling Rate (DSR) estimates the communication defect rate. Marginalizing the scaling rate using the Spectral entity feature selection approach (SEFSA) is applied to select the features and trained to identify with an Artificial neural network classifier (ANN). Based on the attack principles and activities in the 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 produces high performance compared to other systems to identify malicious activities to improve security against cyber-attacks.