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

Survival Tree01:19

Survival Tree

57
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...
57

You might also read

Related Articles

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

Sort by
Same author

SMA-Driven Assistive Hand for Rehabilitation Therapy.

Sensors (Basel, Switzerland)·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

Extracting Optimal Number of Features for Machine Learning Models in Multilayer IoT Attacks.

Badeea Al Sukhni1, Soumya K Manna1, Jugal M Dave2

  • 1School of Engineering, Technology and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Semi-Automated Intrusion Detection System (SAIDS) to combat sophisticated multilayer attacks in Internet of Things (IoT) systems. The SAIDS framework effectively identifies these complex threats with over 94% accuracy using optimized features.

Keywords:
IoT attacksfeature selectionfeature weightinghuman–machine teamingmachine learningmultilayer security

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

Related Experiment Videos

Last Updated: Jun 3, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

Area of Science:

  • Cybersecurity
  • Network Security
  • Internet of Things (IoT) Security

Background:

  • Internet of Things (IoT) systems face escalating security risks from multilayer attacks, leading to data breaches and financial losses.
  • Existing intrusion detection methods for IoT often lack real-world applicability due to outdated datasets and limited adaptive capabilities.
  • Over-reliance on fully automated processes can hinder the reliability of intrusion detection models, highlighting the need for human-machine interaction.

Purpose of the Study:

  • To develop a Semi-Automated Intrusion Detection System (SAIDS) for detecting and identifying multilayer attacks in IoT environments.
  • To enhance mitigation strategies by integrating efficient feature selection, weighting, normalization, visualization, and human-machine interaction.
  • To address the limitations of current research by focusing on adaptive, dynamic approaches and real-world applicability.

Main Methods:

  • Development of a SAIDS framework incorporating feature selection, feature weighting, normalization, visualization, and human-machine interaction.
  • Extraction of an optimal subset of 13 significant features from the 64 available in the Edge-IIoT dataset.
  • Comparative analysis of machine learning classifiers for multilayer attack detection, focusing on the K-Nearest Neighbors (KNN) model.

Main Results:

  • The SAIDS framework successfully identified an optimal set of 13 critical features for multilayer attack detection and classification.
  • The KNN algorithm demonstrated superior performance compared to other classifiers in binary classification tasks.
  • The KNN model achieved an average accuracy exceeding 94% in detecting various multilayer attacks, including UDP, ICMP, HTTP flood, MITM, TCP SYN, XSS, and SQL injection.

Conclusions:

  • The proposed SAIDS framework effectively enhances the detection and classification of multilayer IoT attacks.
  • Integrating human expertise with automated processes improves the reliability of intrusion detection models.
  • The optimized feature set and KNN model provide a robust solution for securing IoT systems against sophisticated cyber threats.