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Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Internet of Things (IoT) systems face significant security challenges due to decentralized architectures and resource-constrained devices.
  • Anomalous network behaviors and data manipulation threaten IoT security and reliability.
  • Machine learning methods are increasingly used for intrusion detection and prevention in IoT.

Purpose of the Study:

  • To propose an advanced, multi-phase anomaly detection framework for IoT networks.
  • To enhance the security and reliability of IoT ecosystems against cyber threats.
  • To develop a scalable and adaptable solution for diverse IoT scenarios.

Main Methods:

  • Data preprocessing using Median-KS Test for noise reduction and data balancing.
  • Optimal feature selection via a Genetic Algorithm with eagle-inspired search strategies.
  • Ensemble classifier combining Decision Tree, Random Forest, and XGBoost algorithms.

Main Results:

  • Achieved 98% accuracy, a 12.5% improvement over existing methods.
  • Increased detection rate to 95% (14% improvement).
  • Reduced false positive rate to 10% (9.3% reduction) and false negative rate to 5% (10.8% reduction).

Conclusions:

  • The proposed framework demonstrates superior effectiveness, reliability, and scalability for securing IoT networks.
  • The multi-step methodology ensures adaptability in handling diverse and evolving cyber threats.
  • The results underscore the framework's potential for real-world IoT security applications.