Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks.

Scientific reports·2025

Same author

An efficient intrusion detection model based on convolutional spiking neural network.

Scientific reports·2024

Same author

Preliminary Stages for COVID-19 Detection Using Image Processing.

Diagnostics (Basel, Switzerland)·2022

Same author

Cyber-Attack Prediction Based on Network Intrusion Detection Systems for Alert Correlation Techniques: A Survey.

Sensors (Basel, Switzerland)·2022

Same author

Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation.

PloS one·2016

Search research articles

Related Experiment Video

Updated: Sep 17, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Enhancing IDS for the IoMT based on advanced features selection and deep learning methods to increase the model

Ahmed Muqdad Alnasrallah¹, Maheyzah Md Siraj², Hanan Ali Alrikabi³

¹Faculty of Education for Pure Sciences, University of Thi-Qar, Nasiriyah, Iraq.

|July 2, 2025

Summary

This study introduces an Intrusion Detection System (IDS) for the Internet of Medical Things (IoMT) using deep learning and feature selection. The model significantly enhances security and efficiency for remote patient monitoring systems.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Sep 17, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Computer Science
Biomedical Engineering
Cybersecurity

Background:

Information technology, particularly the Internet of Things (IoT) and its medical variant (IoMT), is revolutionizing healthcare through remote patient monitoring.
The increasing interconnectivity of IoMT devices presents significant security challenges, necessitating robust Intrusion Detection Systems (IDS).

Purpose of the Study:

To propose and evaluate a novel IDS model for IoMT environments.
To enhance the detection performance and efficiency of security systems in IoMT networks.

Main Methods:

The proposed IDS model integrates Information Gain (IG) and Recursive Feature Elimination (RFE) for feature selection.
A deep autoencoder (DAE) is employed for dimensionality reduction, preserving crucial data.
A deep neural network (DNN) is utilized for classifying network traffic as normal or anomalous.

Main Results:

The model achieved high accuracy (99.93% on WUSTL-EHMS-2020, 99.61% on CICIDS2017) and improved performance metrics (precision, recall, F1 score).
The strategy demonstrated reduced training time and computational complexity.
Statistical validation confirmed stable performance improvement with a p-value of 0.0001.

Conclusions:

The developed IDS model offers superior accuracy and efficiency for IoMT security.
The approach is effective in resource-constrained IoMT environments, enhancing overall system security.
This study highlights the potential of advanced feature selection and deep learning for securing connected healthcare systems.