Ajou University repository

Empirical Distribution Ranking based Decision Tree Algorithm for building Intrusion Detection System in the Internet of Medical Things
Citations

SCOPUS

0

Citation Export

Publication Year
2024-01-01
Journal
Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024, pp.87-92
Keyword
EDRIntrusion detection systemJ48Smart Healthcare
Mesh Keyword
Decision-tree algorithmEmpirical distribution rankingEmpirical distributionsInternet connectivityIntrusion Detection SystemsJ48Patient healthSecurity risksServices and applicationsSmart healthcare
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsArtificial IntelligenceInformation Systems and ManagementModeling and SimulationControl and Optimization
Abstract
The concept of the Internet Of Things (IoT) is becoming more and more popular in many services and applications of the future. One of the notable fields is the Internet of Medical Things (IoMT). The recognition of IoMT is due to its ability to enable Internet connectivity to smart healthcare systems, thus allowing remote monitoring of patients' health, provisioning assistance, and collecting more information about the patient's health status. However, these innovations come at the cost of security risks due to the high vulnerability of these systems. Therefore, it is crucial to develop effective security mechanisms that will be able to protect the confidentiality and privacy of patient data and prevent unauthorized access and attacks. We propose an intrusion detection system (IDS) that leverages the empirical distribution ranking (EDR) based on the J48 classification algorithm for detecting such attacks. The proposed framework selects the optimal features based on the empirical distributed ranks. The EDR ranks the features according to their importance to the target class. The J48 classifier classifies a new class by creating a decision tree from the selected feature values. The proposed method detected security risks with a high accuracy of 99.33% and a precision of 0.99. The testing time in the WUSTL-EHMS-2020 dataset is 0.04s, which meets the requirements of various IoMT-related applications.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37117
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85205947467&origin=inward
DOI
https://doi.org/10.1109/aiot63253.2024.00027
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10677505
Type
Conference
Funding
This work was supported by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991514504).
Show full item record

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Ko, Young-Bae Image
Ko, Young-Bae고영배
Department of Software and Computer Engineering
Read More

Total Views & Downloads

File Download

  • There are no files associated with this item.