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Related Experiment Video

Updated: Jan 15, 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

Enhanced intrusion detection system IoT network security model by feed forward neural network and machine learning.

Abdullah Mujawib Alashjaee1, Fahad Alqahtani2

  • 1Department of Computer Sciences, College of Science, Northern Border University, Arar, Kingdom of Saudi Arabia. abdullah.alashjaee@nbu.edu.sa.

Scientific Reports
|October 15, 2025
PubMed
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This study introduces a hybrid Intrusion Detection System (IDS) using deep learning (DL) and machine learning (ML) for enhanced Internet of Things (IoT) security. The novel model achieves 99% accuracy in detecting cyber threats, outperforming existing methods.

Area of Science:

  • Cybersecurity
  • Network Security
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) networks face escalating cyber threats.
  • Traditional Intrusion Detection Systems (IDS) are limited by resource constraints and evolving attack patterns.
  • Sophisticated attacks in real-time are challenging for current IDS.

Purpose of the Study:

  • To develop a novel hybrid IDS integrating deep learning (DL) and machine learning (ML) for improved IoT security.
  • To enhance attack detection accuracy while minimizing computational overhead in IoT networks.
  • To create a scalable and efficient hybrid IDS model for robust intrusion detection.

Main Methods:

  • Proposed a hybrid IDS combining Feed Forward Neural Networks (FFNN) and XGBoost.
Keywords:
Cyber risksFeed forward neural networksInternet of thingsIntrusion detection systemXGBoost

Related Experiment Videos

Last Updated: Jan 15, 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 Principal Component Analysis (PCA) for feature selection.
  • Trained and evaluated the model on the CIC IoT 2023 dataset for real-time attack data analysis.

    • Main Results:

    • Achieved superior accuracy of 99% in detecting intrusions, surpassing existing IDS techniques.
    • The hybrid FFNN-XGBoost model demonstrated better performance than standalone FFNN and XGBoost classifiers.
    • Significantly improved precision, recall, and F1-score for robust intrusion detection.

    Conclusions:

    • The hybrid FFNN-XGBoost IDS offers a scalable and efficient solution for IoT security.
    • The study provides valuable insights into addressing challenges like dataset imbalance and feature selection in IDS.
    • Findings support future advancements in intrusion detection utilizing DL and ML approaches for IoT environments.