Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model

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Published: Jul 28, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i6.7558
Keywords:
DDoS attack, IoMT, Delay Attack, Logistic Regression, Random Forest, Decision Tress, Stochastic Gradient Distance, Naive Bayes

Main Article Content

Abdullah Ali Jawad Al-Abadi
Laboratory of signals, systems, artificial intelligence and networks (SM@RTS), Digital Research Center of Sfax (CRNS), National School of Engineers of Sfax (ENIS), University of Sfax Sfax, Tunisia
Mbarka Belhaj Mohamed
Laboratory of signals, systems, artificial intelligence and networks (SM@RTS), Digital Research Center of Sfax (CRNS), National School of Engineers of Gabes (ENIG), University of Sfax, Gabes, Tunisia
Ahmed Fakhfakh
Laboratory of signals, systems, artificial intelligence and networks (SM@RTS), Digital Research Center of Sfax (CRNS), National School of Engineers of Sfax (ENIS), University of Sfax, Sfax, Tunisia

Abstract

Internet of Medical Things (IoMT ) refers to the network of medical devices and healthcare systems that are connected to the internet. However, this connectivity also makes IoMT vulnerable to cyberattacks such as DoS and Delay attacks , posing risks to patient safety, data security, and public trust. Early detection of these attacks is crucial to prevent harm to patients and system malfunctions. In this paper, we address the detection and mitigation of DoS and Delay attacks in the IoMT using machine learning techniques. To achieve this objective, we constructed an IoMT network scenario using Omnet++ and recorded network traffic data. Subsequently, we utilized this data to train a set of common machine learning algorithms. Additionally, we proposed an Enhanced Random Forest Classifier for Achieving the Best Execution Time (ERF-ABE), which aims to achieve high accuracy and sensitivity as well as  low execution time for detecting these types of attacks in IoMT networks. This classifier combines the strengths of random forests with optimization techniques to enhance performance. Based on the results, the execution time has been reduced by implementing ERF-ABE, while maintaining high levels of accuracy and sensitivity.

Article Details

How to Cite
Al-Abadi, A. A. J. ., Mohamed, M. B. ., & Fakhfakh, A. . (2023). Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6), 239–247. https://doi.org/10.17762/ijritcc.v11i6.7558
Issue
Vol. 11 No. 6 (2023): June (2023) Issue
Section
Articles

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