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Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based

Ali Saeed Almuflih1,2, Ilyos Abdullayev3, Sergey Bakhvalov4,5

  • 1Department of Industrial Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, Saudi Arabia.

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|November 25, 2024
PubMed
Summary

This study introduces a novel method for detecting unknown cyberattacks in the Internet of Things (IoT). The Binary Snake Optimizer with DL-Enabled Zero-Day Attack Detection and Classification (BSODL-ZDADC) method achieves high accuracy in identifying and classifying these threats.

Keywords:
Binary snake optimizerHyperparameter tuningInternet of thingsIntrusion-detection systemsZero-day attack

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Area of Science:

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

Background:

  • The Internet of Things (IoT) faces escalating cyberattack sophistication, particularly zero-day exploits, posing significant security challenges.
  • Traditional Intrusion Detection Systems (IDS) struggle with unidentified attacks, raising concerns about user data privacy and security.
  • Conventional machine learning (ML) models exhibit limitations in accuracy and recognition rates for anomaly detection.

Purpose of the Study:

  • To develop an advanced method for the enhanced recognition and classification of zero-day attacks in IoT environments.
  • To integrate metaheuristic optimization with deep learning (DL) techniques for superior threat detection capabilities.
  • To address the limitations of existing ML models in handling novel and unidentified cyber threats.

Main Methods:

  • A Binary Snake Optimizer with DL-Enabled Zero-Day Attack Detection and Classification (BSODL-ZDADC) method was proposed.
  • Z-score normalization was employed for data preprocessing.
  • A Binary Snake Optimizer (BSO) was utilized for feature selection to reduce dimensionality and enhance classification.
  • An attention-based bidirectional gated recurrent unit (ABi-GRU) was implemented for zero-day attack recognition.
  • An improved sparrow search algorithm (ISSA) was used for hyperparameter optimization.

Main Results:

  • The BSODL-ZDADC method demonstrated superior performance in identifying and classifying zero-day attacks.
  • Feature selection via BSO effectively reduced data dimensionality and improved classification outcomes.
  • The ABi-GRU model, optimized with ISSA, achieved high accuracy in detecting unknown threats.
  • Experimental validation on the ToN-IoT dataset yielded an accuracy of 98.28%.

Conclusions:

  • The BSODL-ZDADC method offers a robust and accurate solution for detecting zero-day cyberattacks in IoT networks.
  • The integration of metaheuristics (BSO, ISSA) with deep learning (ABi-GRU) significantly enhances intrusion detection capabilities.
  • The proposed approach effectively overcomes the limitations of conventional ML models in identifying novel threats, ensuring better data privacy and security.