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Binary Hunter-Prey Optimization with Machine Learning-Based Cybersecurity Solution on Internet of Things Environment.

Adil O Khadidos1, Zenah Mahmoud AlKubaisy2,3, Alaa O Khadidos4,5

  • 1Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a novel machine learning method to detect phishing attacks in the Internet of Things (IoT). The Binary Hunter-Prey Optimization with Machine Learning-based Phishing Attack Detection (BHPO-MLPAD) effectively identifies and mitigates these security threats.

Keywords:
Internet of Thingsfeature selectionhunter prey optimizationmachine learningphishing attack

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) facilitates connectivity for everyday objects, leading to widespread technological integration.
  • However, the rapid expansion of IoT devices introduces significant security vulnerabilities, making them susceptible to cyber threats.
  • Phishing attacks, aimed at fraudulent data extraction, are increasingly targeting IoT devices, posing a growing risk.

Purpose of the Study:

  • To develop an effective method for detecting phishing attacks specifically within the Internet of Things (IoT) environment.
  • To enhance the security of IoT networks and devices against sophisticated phishing scams.
  • To improve the accuracy and efficiency of phishing attack detection using machine learning and optimization algorithms.

Main Methods:

  • A Binary Hunter-Prey Optimization with Machine Learning-based Phishing Attack Detection (BHPO-MLPAD) method is proposed.
  • The BHPO algorithm is utilized for optimal feature selection, identifying crucial data points for attack detection.
  • A Cascaded Forward Neural Network (CFNN) model, optimized by the Variable Step Fruit Fly Optimization (VFFO) algorithm, performs the phishing attack classification.

Main Results:

  • The BHPO-MLPAD technique demonstrated superior performance in identifying phishing attacks compared to existing methods.
  • The method achieved high accuracy in feature selection and classification tasks within the IoT context.
  • Evaluation on a benchmark dataset confirmed the effectiveness of the proposed approach across various performance metrics.

Conclusions:

  • The BHPO-MLPAD method offers a robust solution for detecting phishing attacks in IoT environments.
  • The integration of advanced optimization and machine learning techniques significantly improves cybersecurity for connected devices.
  • This research contributes to securing the expanding landscape of the Internet of Things against evolving cyber threats.