An intrusion detection system for health-care system using machine and deep learning

Abstract

Purpose

One of the important key components of health care–based system is a reliable intrusion detection system. Traditional techniques are not adequate to handle complex data. Also, the diversified intrusion techniques cannot meet current network requirements. Not only the data is getting increased but also the attacks are increasing very rapidly. Deep learning and machine learning techniques are very trending in the area of research in the area of network security. A lot of work has been done in this area by still evolutionary algorithms along with machine learning is very rarely explored. The purpose of this study is to provide novel deep learning framework for the detection of attacks.

Design/methodology/approach

In this paper, novel deep learning is the framework is proposed for the detection of attacks. Also, a comparison of machine learning and deep learning algorithms is provided.

Findings

The obtained results are more than 99% for both the data sets.

Research limitations/implications

The diversified intrusion techniques cannot meet current network requirements.

Practical implications

The data is getting increased but also the attacks are increasing very rapidly.

Social implications

Deep learning and machine learning techniques are very trending in the area of research in the area of network security.

Originality/value

Novel deep learning is the framework is proposed for the detection of attacks.

Citation

Pande, S., Khamparia, A. and Gupta, D. (2022), "An intrusion detection system for health-care system using machine and deep learning", World Journal of Engineering, Vol. 19 No. 2, pp. 166-174. https://doi.org/10.1108/WJE-04-2021-0204