Jove
Visualize
Contact Us

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

SSDBFAN: Scalable and Secure Cluster-Based Data Aggregation with Blockchain for Flying Ad Hoc Networks.

Sensors (Basel, Switzerland)·2026
Same author

Robust Federated-Learning-Based Classifier for Smart Grid Power Quality Disturbances.

Sensors (Basel, Switzerland)·2025
Same author

Hyperparameter optimization of XGBoost and hybrid CnnSVM for cyber threat detection using modified Harris hawks algorithm.

PeerJ. Computer science·2025
Same author

Hardware Acceleration-Based Privacy-Aware Authentication Scheme for Internet of Vehicles Using Physical Unclonable Function.

Sensors (Basel, Switzerland)·2025
Same author

Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks.

Sensors (Basel, Switzerland)·2024
Same author

Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning.

Sensors (Basel, Switzerland)·2024
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
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 Experiment Video

Updated: Jun 11, 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

483

Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems.

Engy El-Shafeiy1, Walaa M Elsayed2, Haitham Elwahsh3

  • 1Department of Computer Science, Faculty of Computers & Artificial Intelligence, University of Sadat City, Sadat City 32897, Egypt.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

A new deep learning intrusion detection system (IDS), DCGR_IoT, effectively secures Internet of Things (IoT) networks. It achieves 99.2% accuracy in detecting cyber-attacks by analyzing network traffic patterns.

Keywords:
anomaly detectioncomplex gated recurrent networks (CGRNs)convolutional neural networks (CNN)deep neural learninginternet of things (IoT)intrusion detection system (IDS)

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

Related Experiment Videos

Last Updated: Jun 11, 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

483
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

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

Published on: October 27, 2023

1.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

Area of Science:

  • Cybersecurity
  • Network Security
  • Artificial Intelligence

Background:

  • The rapid expansion of the Internet of Things (IoT) necessitates advanced security solutions.
  • Traditional Intrusion Detection Systems (IDS) struggle with the unique challenges of IoT environments, including device diversity and real-time detection needs.

Purpose of the Study:

  • To propose DCGR_IoT, a novel deep neural learning-based IDS designed for bidirectional IoT communication networks.
  • To enhance anomaly detection capabilities within IoT environments.

Main Methods:

  • Utilizing Convolutional Neural Networks (CNN) for spatial feature extraction and data filtering.
  • Employing Complex Gated Recurrent Networks (CGRNs) for temporal feature extraction and multidimensional feature subset construction.
  • Leveraging CGRNs to create detailed spatial representations of network traffic for critical feature extraction.

Main Results:

  • DCGR_IoT demonstrated high effectiveness on benchmark datasets (UNSW-NB15, KDDCup99, IoT-23).
  • Achieved a superior detection accuracy rate of 99.2% against sophisticated cyber-attacks.
  • Validated the system's capability for efficient and accurate intrusion detection in IoT networks.

Conclusions:

  • DCGR_IoT presents a robust and effective solution for safeguarding IoT networks.
  • The proposed system addresses the limitations of conventional IDS in dynamic IoT environments.
  • Highlights the potential of deep learning models for advanced IoT security.