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Related Experiment Video

Updated: Aug 19, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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A novel hybrid optimization enabled robust CNN algorithm for an IoT network intrusion detection approach.

Ahmed Bahaa1,2, Abdalla Sayed1, Laila Elfangary1

  • 1Faculty of Computers and Artificial Intelligence, Department of Information Systems, Helwan University, Helwan, Egypt.

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Summary

This study introduces a novel hybrid meta-heuristic algorithm (APSO-WOA) to optimize deep learning models for detecting Internet of Things (IoT) network attacks, significantly improving detection accuracy and performance.

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • The proliferation of Internet of Things (IoT) devices has led to an increase in network attacks, such as denial of service and flooding, disrupting device functionality.
  • Existing methods for detecting these attacks often struggle with the scale and complexity of modern IoT networks.

Purpose of the Study:

  • To propose a novel hybrid meta-heuristic algorithm, Adaptive Particle Swarm Optimization-Whale Optimizer Algorithm (APSO-WOA), for optimizing the hyperparameters of Convolutional Neural Networks (CNNs).
  • To evaluate the effectiveness of the proposed APSO-WOA-CNN model in detecting multi-type IoT network attacks and compare its performance against other optimization algorithms.

Main Methods:

  • Developed a hybrid meta-heuristic algorithm, APSO-WOA, to optimize CNN hyperparameters.
  • Defined the fitness function of the APSO-WOA algorithm as the cross-entropy loss on the validation set during CNN model training.
  • Evaluated the APSO-WOA-CNN model against APSO-CNN, Support Vector Machine (SVM), and Feedforward Neural Network (FNN) algorithms.

Main Results:

  • The APSO-WOA-CNN algorithm demonstrated superior performance in detecting multi-type IoT network attacks compared to FNN with manual settings, APSO-CNN, SVM, and FNN.
  • Achieved significant improvements over the APSO-CNN algorithm: 1.25% increase in accuracy, 1% in average precision, 11% in the kappa coefficient, 1.2% reduction in Hamming loss, and 2% increase in Jaccard similarity coefficient.
  • The APSO-CNN algorithm itself showed the best performance among the compared algorithms, with APSO-WOA-CNN further enhancing these results.

Conclusions:

  • The proposed APSO-WOA-CNN model is effective and reliable for detecting diverse IoT network attacks.
  • Hyperparameter optimization using the APSO-WOA algorithm significantly enhances the performance of CNNs for network intrusion detection.
  • The study highlights the potential of hybrid meta-heuristic approaches in bolstering IoT network security.