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Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection.

M Karthikeyan1, D Manimegalai2, Karthikeyan RajaGopal3

  • 1Centre for Advanced Wireless Integrated Technology, Chennai Institute of Technology, Chennai, India. karthickm37@gmail.com.

Scientific Reports
|January 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Firefly Algorithm-Machine Learning (FA-ML) technique for enhanced intrusion detection in Wireless Sensor Networks (WSN) and the Internet of Things (IoT). The FA-ML method achieves 99.34% accuracy, significantly improving WSN-IoT security.

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Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning Applications

Background:

  • Wireless Sensor Networks (WSN) and the Internet of Things (IoT) are increasingly integrated for enhanced data analysis and automation.
  • Securing these interconnected WSN and IoT systems is critical for reliability and safety.
  • Existing security measures require advancement to address the complexities of integrated WSN-IoT environments.

Purpose of the Study:

  • To develop and evaluate a novel security technique for WSN-IoT systems.
  • To enhance intrusion detection accuracy through the synergy of machine learning and bio-inspired algorithms.
  • To introduce a new security-oriented optimization approach for interconnected networks.

Main Methods:

  • Proposed a Firefly Algorithm-Machine Learning (FA-ML) technique for intrusion detection.
  • Utilized a Support Vector Machine (SVM) model for classification.
  • Employed the Grey Wolf Optimizer (GWO) algorithm for parameter tuning of the SVM model.
  • Simulated experimental evaluations using the NSL-KDD Dataset.

Main Results:

  • The FA-ML technique achieved a maximum intrusion detection accuracy of 99.34%.
  • This significantly outperformed other models, with KNN-PSO achieving 96.42% and XGBoost achieving 95.36% accuracy.
  • Demonstrated the effectiveness of combining machine learning with the Firefly Algorithm for robust security.

Conclusions:

  • The FA-ML technique represents a significant advancement in WSN-IoT security.
  • It offers a powerful and intelligent solution for bolstering intrusion detection capabilities.
  • The findings validate the potential of bio-inspired algorithms and machine learning in securing modern interconnected systems.