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Updated: Jun 18, 2025

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

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Published on: December 15, 2023

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Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection.

Mohammed Tawfik1

  • 1Faculty of Computer and Information Technology, Sana'a University, Sana'a, Yemen.

Plos One
|August 1, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for intrusion detection in Internet of Things (IoT) and fog computing environments. The system achieves over 99% accuracy in identifying cyber threats, enhancing network security.

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Area of Science:

  • Cybersecurity
  • Network Security
  • Applied Machine Learning

Background:

  • Internet of Things (IoT) and fog computing introduce significant security vulnerabilities.
  • Traditional intrusion detection systems face limitations due to resource constraints in fog environments.
  • Effective anomaly detection is crucial for securing modern network infrastructures.

Purpose of the Study:

  • To propose a novel, efficient, and accurate intrusion detection framework for fog and IoT networks.
  • To address the challenges posed by limited computing resources at fog nodes.
  • To enhance the detection of evolving cyber-attacks in distributed network architectures.

Main Methods:

  • Integration of stacked autoencoders for feature extraction and dimensionality reduction.
  • Utilization of CatBoost for feature refinement and predictive selection.
  • Development of an optimized ensemble model combining transformer-CNN-LSTM for comprehensive traffic analysis.
  • Implementation of edge preprocessing and cloud-based ensemble learning pipelines.

Main Results:

  • Achieved over 99% accuracy in threat detection across multiple benchmark datasets (NSL-KDD, UNSW-NB15, AWID).
  • Demonstrated efficient and accurate anomaly detection through integrated edge and cloud processing.
  • Validated the framework's effectiveness in traditional, hybrid, and wireless environments.

Conclusions:

  • The proposed framework offers a viable solution for securing fog and IoT infrastructure against sophisticated cyber-attacks.
  • The hybrid approach effectively balances computational load between edge and cloud resources.
  • The system provides robust protection against continuously evolving network threats.