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Updated: Oct 3, 2025

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LT-FS-ID: Log-Transformed Feature Learning and Feature-Scaling-Based Machine Learning Algorithms to Predict the

Abhilash Singh1, J Amutha2, Jaiprakash Nagar3

  • 1Fluvial Geomorphology and Remote Sensing Laboratory, Indian Institute of Science Education and Research Bhopal, Bhopal 462066, India.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
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This summary is machine-generated.

This study introduces a new method for predicting intrusion detection barriers in Wireless Sensor Networks (WSNs). The novel approach accurately forecasts barrier numbers, enhancing border security with explainable AI.

Area of Science:

  • Computer Science
  • Network Security
  • Machine Learning

Background:

  • Advancements in computational power and explainable AI facilitate rapid intrusion detection.
  • Wireless Sensor Networks (WSNs) are crucial for real-time monitoring and security applications.

Purpose of the Study:

  • To develop a novel approach for accurately predicting the number of barriers needed for intrusion detection and prevention in WSNs.
  • To enhance the efficiency and accuracy of border security systems using WSNs and machine learning.

Main Methods:

  • Monte Carlo simulation was used to extract key features: Region of Interest (RoI) area, sensor sensing range, sensor transmission range, and sensor count.
  • Feature importance and sensitivity analyses were performed to assess feature relevance and risk.
Keywords:
WSNsfeature learningintrusion detectionmachine learningsupport vector regression

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  • Log transformation and feature scaling were applied before training a tuned Support Vector Regression (SVR) model, termed the LT-FS-SVR model.

    • Main Results:

    • The LT-FS-SVR model demonstrated high accuracy in predicting the number of barriers, achieving a correlation coefficient (R) of 0.98.
    • The model yielded a Root Mean Square Error (RMSE) of 6.47 and a bias of 12.35.
    • Comparative analysis showed the proposed LT-FS-SVR model outperformed benchmark algorithms including Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Artificial Neural Network (ANN), and Random Forest (RF).

    Conclusions:

    • The proposed LT-FS-SVR model offers a highly accurate and effective solution for predicting barrier requirements in WSN-based intrusion detection systems.
    • This research contributes to improving border security by leveraging explainable AI and advanced machine learning techniques for faster and more precise threat prevention.
    • The study validates the superiority of the developed model over existing methods, paving the way for practical implementation in real-world security scenarios.