Open Access

ARTICLE

Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

by Phong Thanh Nguyen¹, Vy Dang Bich Huynh², Khoa Dang Vo¹, Phuong Thanh Phan¹, Mohamed Elhoseny³, Dac-Nhuong Le^4,5,*

1 Department of Project Management, Ho Chi Minh City Open University, Ho Chi Minh City, 700000, Vietnam
2 Department of Learning Material, Ho Chi Minh City Open University, Ho Chi Minh City, 700000, Vietnam
3 Faculty of Computers and Information, Mansoura University, Dakahlia Governorate, 35516, Egypt
4 Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam
5 Faculty of Information Technology, Duy Tan University, Danang, 550000, Vietnam

* Corresponding Author: Dac-Nhuong Le. Email:

(This article belongs to the Special Issue: Intelligent Decision Support Systems for Complex Healthcare Applications)

Computers, Materials & Continua 2021, 66(3), 2555-2571. https://doi.org/10.32604/cmc.2021.012941

Received 18 July 2020; Accepted 24 August 2020; Issue published 28 December 2020

Abstract

Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists to merge diverse techniques using decision-based multimodal fusion process. In this view, this research article presents a new multimodal fusion-based IDS to secure the healthcare data using Spark. The proposed model involves decision-based fusion model which has different processes such as initialization, pre-processing, Feature Selection (FS) and multimodal classification for effective detection of intrusions. In FS process, a chaotic Butterfly Optimization (BO) algorithm called CBOA is introduced. Though the classic BO algorithm offers effective exploration, it fails in achieving faster convergence. In order to overcome this, i.e., to improve the convergence rate, this research work modifies the required parameters of BO algorithm using chaos theory. Finally, to detect intrusions, multimodal classifier is applied by incorporating three Deep Learning (DL)-based classification models. Besides, the concepts like Hadoop MapReduce and Spark were also utilized in this study to achieve faster computation of big data in parallel computation platform. To validate the outcome of the presented model, a series of experimentations was performed using the benchmark NSLKDDCup99 Dataset repository. The proposed model demonstrated its effective results on the applied dataset by offering the maximum accuracy of 99.21%, precision of 98.93% and detection rate of 99.59%. The results assured the betterment of the proposed model.

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