11th Annual International Conference on Industrial Engineering and Operations Management

The Impact of Intrusion Detection Systems Upon Healthcare Environments: A Research Review

Tasfia Bari & Munther Abualkibash
Publisher: IEOM Society International
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Track: Cyber Security
Abstract

 

  The Impact of Intrusion Detection Systems Upon Healthcare Environments: A Research Review

Tasfia Bari M.S., PhD Candidate

College of Technology

Eastern Michigan University

900 Oakwood St, Ypsilanti, MI 48197

TBari@emich.edu  

Dr. Munther Abualkibash 

Information Security and Applied Computing

Eastern Michigan University 

202K Roosevelt 

734.487.2285 

mabualki@emich.edu

Abstract 

    As healthcare systems throughout the globe face an influx of new information exchange due from a variety of different network transferences, the caution that arises from utilizing such technologies can distinguish which healthcare systems are equipped to handle potential cybersecurity attacks and breaches in comparison to those who are not. This study seeks to review research that is currently available pertaining to the impact of Intrusion Detection Systems (IDS) upon different healthcare systems and the networks in which they exchange patient health information (PHI) and electronic health records (EHR) pertaining to their hospital and clinical experiences. The growing concern for patient information assurance is brought forth and substantiated as more and more healthcare providers switch to digital platforms for storing patient information and their staff exchange information for efficient healthcare practices. Whilst different researchers approach IDS from different perspectives in regard to healthcare, the outcome suggests neural network approaches such as autoencoding and fuzzy logic can help dispel potential breaches and network security threats from intercepting PHI as it is shared. 

  • Keywords 

Intrusion, Detection, System, Healthcare, Network 

Biographies:

Dr. Munther Abualkibash 

  Abualkibash is a professor and graduate coordinator within the Eastern Michigan University College of Technology. His interests and expertise include computer and network security, cloud computing, machine learning and parallel and distributed systems. He earned his B.S. in Computer Science from Al-Ahliyaa Amman University in Amman, Jordan. From there on, he received his master’s degree from the University of Bridgeport, in Bridgeport, Connecticut. There he also earned his Ph.D. in computer science and engineering.

Tasfia Bari M.S.  

   Bari is a PhD candidate and graduate research assistant in Eastern Michigan University’s College of Technology. She earned her Bachelor of Science at Eastern Michigan University in Ypsilanti, Michigan. She has graduate research experience throughout her time in the College of Technology as both a master’s and Doctoral candidate. She is currently working towards earning her PhD while conducting research as a graduate assistant under the supervision of Dr. Munther Abualkibash.

Published in: 11th Annual International Conference on Industrial Engineering and Operations Management, Singapore, Singapore

Publisher: IEOM Society International
Date of Conference: March 7-11, 2021

ISBN: 978-1-7923-6124-1
ISSN/E-ISSN: 2169-8767