Design of Secure Blockchain Convolution Neural Network Architecture for Detection Malware Attacks

Abstract

Nowadays, Cyberattack continues to target the applications and networks more than past with different and advance ways like programming complex format of malware that it executes unauthorized action on the targeted system, so it is needed to develop and deploy advance method to these kind of attacks for detecting correctly with a trusted and a better accuracy. Therefore, the recent solutions to detect malware attacks focuses on new advance technologies like Deep learning and Machine learning concepts. In this paper we have developed secure blockchain convolution (SBC) Algorithm that provides a better way of analyzing malware data with effectiveness and accuracy. The deep learning concept does not involve in a method to identify the trust while the process is led to extraction of the features as it can be infected by the intervention of human or a trained system. Therefore, According to research which is done towards blockchain, it features as authentication function, immutable property, information privacy and safety helps in deployment of Convolution Neural Network method with better detection. Blockchain has a decentralized structure which is able to record the data between various parties and it helps in preventing the manipulation when the deep learning concept is applied and the higher detection accuracy is received in the limited time.