Big Data Analysis and Management of Healthcare Systems for Hacker Detection Based on Google Net Convolutional Neural Network

Abstract

In recent times, Hacking has turn out to be more unfavorable than ever in all life fields, including the healthcare systems, with an increasing usage of information technology. By the expansion of technology development, the attacks number is too rising every few months in an exponential manner, which in turn makes the conventional IDS incapable to perceive. A healthcare system network intrusion detection method is proposed depending on the Google NET convolution neural network (Google NET). In healthcare system databases, intrusion detection (KDDs) can be seen as a search issue, which might be solved with the use of Google NET CNN algorithms. After pre-processing and characterizing the healthcare system data (including Electronic Health Records (EHR), Medical imaging data, Electronic Medical Records (EMR), etc.), the Google NET CNN model is used to simulate the intrusion into the healthcare system data. The low-level data intrusion is signified conceptually as the superior features with Google NET CNN, which in turn extracts the sample features separately, and by using MFO, network parameter is optimized (algorithm of optimization to meet the representation. At last, a sample test is conducted for the detection of healthcare system network intrusion behavior. The simulation outcome illustrate that the proposed technique has high accuracy on detection and a lower false-positive rate along with true positive rate.