Effective cyber security system to secure optical data based on deep learning approach for healthcare application

Abstract

In the recent days, data’s plays a significant role and its enormous quantity are extracted based on various IoT devices. Various IoT devices generates huge data and it is processed to extract the knowledge data through data analytics. To process those data, which are extracted and needs Deep Learning (DL) model as it needs large number of input and its attributes. The deep learning models helps in classifying the data and process those data generated from IoT and those useful optical data will be useful to those consumers. As huge data are circulated and it contains some private information’s related to optics to be secure from the third parties along with effective data processing. The modified deep learning approach based on Cyber Physical Systems (CPS) is propose to effectively process those IoT based data with enhanced data security. Based on deep learning model, the data access algorithm helps to process the raw data generated from IoT time to time and it helps to convert the data to knowledge data. Then the data are secured based on policy access control against various attacks like DoS and DDoS. Based on the performance analysis, the approach helps to provide effective classification of data and reliable data’s to be maintained. Then security on IoT data to be verified against DoS and DDoS attacks on cyber physical system on real time applications.