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 Experiment Video

Updated: Jan 17, 2026

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

1.0K

A deep learning/machine learning approach for anomaly based network intrusion detection.

Reem Almuhanna1, Samia Dardouri1,2

  • 1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqraa, Saudi Arabia.

Frontiers in Artificial Intelligence
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

492
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
492

You might also read

Related Articles

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

Sort by
Same author

Brain Tumor Segmentation Using U-Net With ResNet50 Encoder for Enhanced MRI Analysis.

International journal of biomedical imaging·2026
Same author

CERV-Score: A Hybrid Machine Learning Framework for Cervical Cancer Risk Prediction Using Integrated Clinical and Genomic Data.

International journal of telemedicine and applications·2026
Same author

A Lesion-Aware Patch Sampling Approach with EfficientNet3D-UNet for Robust Multiple Sclerosis Lesion Segmentation.

Journal of imaging·2025
Same author

Enhancing credit card fraud detection using traditional and deep learning models with class imbalance mitigation.

Frontiers in artificial intelligence·2025
Same author

Fundus Image-Based Eye Disease Detection Using EfficientNetB3 Architecture.

Journal of imaging·2025
Same author

A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging.

Pulmonary medicine·2025
Same journal

Performance of large language models as an information resource on functional hypothalamic amenorrhea for patients and healthcare professionals.

Frontiers in artificial intelligence·2026
Same journal

<i>S</i> <sup>3</sup>Net: a Synthesis-Segmentation-Spiking Network for Alzheimer's disease detection and segmentation.

Frontiers in artificial intelligence·2026
Same journal

Machine learning-based insurance risk assessment pipeline for natural disaster prediction and claims estimation.

Frontiers in artificial intelligence·2026
Same journal

Early Alzheimer's risk detection via diffusion tensor imaging using a few-shot multichannel attention residual learning network.

Frontiers in artificial intelligence·2026
Same journal

An interpretable machine learning framework for classifying human and machine translations across genres.

Frontiers in artificial intelligence·2026
Same journal

AI-driven financial risk management in complex mobile economies: a contextual-technology fit and security reassurance model.

Frontiers in artificial intelligence·2026
See all related articles

This study introduces a hybrid anomaly-based Network Intrusion Detection System (NIDS) using multiple AI models. The advanced system achieves near-perfect performance in detecting cybersecurity threats, enhancing network security.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • Cybersecurity threats are increasing in complexity and frequency.
  • Advanced detection systems are needed for known and emerging attacks.
  • Anomaly-based Network Intrusion Detection Systems (NIDS) are crucial for network defense.

Purpose of the Study:

  • To develop a hybrid anomaly-based NIDS.
  • To integrate multiple machine learning and deep learning algorithms.
  • To improve the detection of diverse cybersecurity threats.

Main Methods:

  • Utilized XGBoost, Random Forest, Graph Neural Networks (GNN), Long Short-Term Memory (LSTM), and Autoencoders.
  • Trained on over 5.6 million network traffic records with extensive preprocessing.
Keywords:
GNNXGBoostautoencodercybersecuritydeep learningensemble learningmachine learningnetwork intrusion detection system

Related Experiment Videos

Last Updated: Jan 17, 2026

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

1.0K
  • Employed Synthetic Minority Over-sampling Technique (SMOTE) and a weighted soft-voting ensemble strategy.

    • Main Results:

    • Achieved near-perfect accuracy, precision, recall, and F1-scores on the primary dataset.
    • Validated performance using 5-fold cross-validation.
    • Demonstrated strong generalizability and robustness on an independent benchmark dataset.

    Conclusions:

    • The hybrid ensemble framework significantly enhances intrusion detection capabilities.
    • The proposed NIDS is effective in complex and dynamic network environments.
    • The system shows high potential for real-world cybersecurity applications.