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Deep learning with leagues championship algorithm based intrusion detection on cybersecurity driven industrial IoT

Saud S Alotaibi1, Turki Ali Alghamdi2

  • 1Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia. ssotaibi@uqu.edu.sa.

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Summary

This study introduces a novel cyberattack detection method for the Internet of Things (IoT). The CLAFS-ODLCD technique achieves 99.48% accuracy, enhancing IoT security against evolving threats.

Keywords:
CybersecurityDeep learningFeature selectionInternet of thingsLeagues championship algorithm

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Area of Science:

  • Cybersecurity and Network Engineering
  • Artificial Intelligence and Machine Learning
  • Internet of Things (IoT) Security

Background:

  • Internet of Things (IoT) networks face significant cybersecurity risks due to resource constraints, necessitating robust intrusion detection systems (IDS).
  • Effective cyberattack detection is vital for protecting sensitive data, user privacy, and critical infrastructure in interconnected environments.
  • Deep learning (DL) offers advanced capabilities for real-time analysis of digital activities to identify and respond to cyber threats.

Purpose of the Study:

  • To propose a novel technique, CLAFS-ODLCD, for enhanced cyberattack detection and classification within IoT infrastructure.
  • To improve the resilience and security of the digital ecosystem against sophisticated cyber threats.
  • To validate the efficacy of the proposed method using a standard dataset and compare its performance against existing models.

Main Methods:

  • The CLAFS-ODLCD technique employs linear scaling normalization (LSN) for data pre-processing.
  • Optimal feature selection is performed using the League Championship Algorithm (LCA).
  • Cyberattack detection and classification are achieved through a stacked sparse autoencoder (SSAE) model, with hyperparameters optimized by the Hunger Games Search (HGS) algorithm.

Main Results:

  • The CLAFS-ODLCD method demonstrated superior performance in identifying and classifying cyberattacks in IoT networks.
  • Empirical analysis on the WSN-DS dataset showed a remarkable accuracy of 99.48%.
  • The proposed technique significantly outperformed existing cyberattack detection models.

Conclusions:

  • The CLAFS-ODLCD technique provides a highly accurate and effective solution for securing IoT environments against cyber threats.
  • The integration of LCA for feature selection and SSAE with HGS optimization offers a robust framework for deep learning-based intrusion detection.
  • This research contributes to strengthening the cybersecurity posture of the digital ecosystem in the face of evolving security challenges.