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A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial

Osama Bassam J Rabie1,2, Shitharth Selvarajan3,4, Tawfiq Hasanin1

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.

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This study introduces an intelligent security framework using Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification to enhance Internet of Things (IoT) cybersecurity against cyber-attacks.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Internet of Things (IoT) devices are increasingly prevalent but vulnerable to cyber-attacks.
  • Current security measures and intrusion detection methods for IoT lack sufficient accuracy and efficiency.
  • Effective intrusion detection is crucial for protecting IoT systems from malicious threats.

Purpose of the Study:

  • To develop a novel, simple, and intelligent security framework for protecting IoT systems from cyber-attacks.
  • To enhance the accuracy and efficiency of intrusion detection in IoT environments.
  • To improve the overall security posture of the Internet of Things.

Main Methods:

  • A hybrid approach combining Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification is proposed.
  • Data preprocessing and normalization are performed to create a balanced IoT dataset.
  • DRF optimization is used to tune features for improved detection accuracy, faster training, and reduced error rates.
  • The DBRF model classifies normal and attacking data flows using the optimized features.

Main Results:

  • The proposed DRF-DBRF security model demonstrated effective performance across five popular IoT benchmarking datasets.
  • The integration of DRF optimization significantly improved feature selection and classifier performance.
  • The framework achieved higher detection accuracy and efficiency compared to conventional methods.

Conclusions:

  • The developed DRF-DBRF security framework offers a robust solution for enhancing IoT security against cyber-attacks.
  • The proposed methodology effectively addresses the limitations of existing intrusion detection systems.
  • This approach represents a significant advancement in securing the Internet of Things.