Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

105
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
105
Types Of Transformers01:16

Types Of Transformers

987
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
987
Classification of Systems-I01:26

Classification of Systems-I

191
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
191
Instrument Transformers01:23

Instrument Transformers

90
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
90
Survival Tree01:19

Survival Tree

88
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
88
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Correction: Okey et al. BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning. <i>Sensors</i> 2022, <i>22</i>, 7409.

Sensors (Basel, Switzerland)·2025
Same author

Quantum-assisted federated intelligent diagnosis algorithm with variational training supported by 5G networks.

Scientific reports·2024
Same author

Developing and evaluating the impact of a small group communication programme in improving the entrepreneurial competence and economic self-efficacy of smallholder farmers with art skills.

PloS one·2023
Same author

BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems Using Boosted Ensemble Machine Learning.

Sensors (Basel, Switzerland)·2022
Same author

Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning.

Sensors (Basel, Switzerland)·2021
Same author

Enhanced Routing Algorithm Based on Reinforcement Machine Learning-A Case of VoIP Service.

Sensors (Basel, Switzerland)·2021
Same journal

Characterization of genomic diversity in bacteriophages infecting Rhodococcus.

PloS one·2026
Same journal

Effectiveness of the Responding to Experienced and Anticipated Discrimination (READ) training on reducing stigma for medical students in Tunisia.

PloS one·2026
Same journal

Cell-cell junction gene signatures as subtype-specific prognostic biomarkers in breast cancer.

PloS one·2026
Same journal

GC-MS based tentative identification of γ-sitosterol from Brassica nigra seeds and evaluation of its anticancer potential: An integrated in vitro and in silico study.

PloS one·2026
Same journal

Ad-based social media interventions increase belief accuracy and generate pro-social opinions among non-news readers.

PloS one·2026
Same journal

Negotiating knowledge: The role of network hedging in the production of high-impact science.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jul 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

565

Attentive transformer deep learning algorithm for intrusion detection on IoT systems using automatic Xplainable

Demóstenes Zegarra Rodríguez1, Ogobuchi Daniel Okey2, Siti Sarah Maidin3

  • 1Department of Computer Science, Federal University of Lavras, Minas Gerais, Brazil.

Plos One
|October 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces TabNet-IDS, an intrusion detection system for the Internet of Things (IoT) that uses attentive mechanisms for feature selection. It achieves high accuracy in detecting network threats on common tabular datasets.

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

Related Experiment Videos

Last Updated: Jul 13, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

565
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things (IoT) Security

Background:

  • The proliferation of Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems increases security risks, particularly from denial of service (DoS) and distributed denial of service (DDoS) attacks.
  • Existing intrusion detection systems (IDS) often struggle with the tabular data format common in machine learning tasks and face challenges in model explainability and feature selection.

Purpose of the Study:

  • To propose an intelligent intrusion detection system (IDS) for IoT security that addresses the limitations of traditional deep learning models on tabular data.
  • To develop a model that utilizes attentive mechanisms for automatic salient feature selection and provides explainable results.

Main Methods:

  • Implementation of the TabNet-IDS model using the TabNet algorithm within the PyTorch deep-learning framework.
  • Utilizing attentive mechanisms within TabNet for automatic feature selection and enhanced model interpretability.
  • Evaluation of the model's performance on benchmark datasets: CIC-IDS2017, CSE-CICIDS2018, and CIC-DDoS2019.

Main Results:

  • The TabNet-IDS model achieved high accuracy rates on the tested datasets: 97% on CIC-IDS2017, 95% on CSE-CICIDS2018, and 98% on CIC-DDoS2019.
  • Demonstrated the effectiveness of the TabNet architecture for intrusion detection in IoT environments using tabular data.
  • The model's attentive mechanisms provided explainable feature selection, contributing to better model understanding.

Conclusions:

  • TabNet is a viable and effective deep learning architecture for intrusion detection on tabular datasets in IoT security.
  • The proposed TabNet-IDS offers a robust solution for enhancing IoT security by accurately detecting network threats with explainable insights.
  • Future work can explore further optimizations and applications of attention-based models for advanced cybersecurity challenges.