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Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach.

Thuan Minh Nguyen1, Hanh Hong-Phuc Vo1, Myungsik Yoo2

  • 1Department of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
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A new Genetic Sacrificial Whale Optimization (GSWO) method enhances intrusion detection systems (IDSs) in wireless sensor networks (WSNs). GSWO-CatBoost achieves superior accuracy and is significantly faster than deep learning for real-time threat detection.

Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Intrusion detection systems (IDSs) are crucial for wireless sensor network (WSN) security.
  • Effective feature selection (FS) is vital for IDS performance but often faces limitations.
  • Conventional methods can struggle with premature convergence and balancing exploration/exploitation.

Purpose of the Study:

  • To introduce a novel feature selection and classification approach for WSN intrusion detection.
  • To enhance the efficacy and efficiency of IDSs using advanced optimization and machine learning.
  • To overcome the limitations of existing methods, particularly premature convergence in optimization algorithms.

Main Methods:

  • Developed the Genetic Sacrificial Whale Optimization (GSWO) algorithm, combining genetic algorithms (GA) and whale optimization algorithms (WOA).
Keywords:
feature selectionhyperparameter optimizationintrusion detectionmachine learningwireless sensor networks

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  • Incorporated a three-population division strategy and conditional inherited choice (CIC) to improve WOA's global search and avoid premature convergence.
  • Utilized the CatBoost model for classification, enhanced with GSWO for hyperparameter fine-tuning via quantization.

    • Main Results:

    • The GSWO-CatBoost approach demonstrated superior performance across multiple benchmark datasets (WSN-DS, WSNBFSF, NSL-KDD, CICIDS2017).
    • Achieved high accuracy rates: 99.65% (WSN-DS), 99.99% (WSNBFSF), 99.76% (NSL-KDD), and 99.74% (CICIDS2017).
    • Exhibited significantly faster inference times, nearly 100x quicker than deep learning methods, indicating real-time applicability.

    Conclusions:

    • The proposed GSWO-CatBoost method offers a highly accurate and efficient solution for intrusion detection in WSNs.
    • The novel GSWO algorithm effectively balances exploration and exploitation, enhancing global search capabilities.
    • The approach proves versatile and accurate across diverse datasets, highlighting its practical value for network security.