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Enhancing malware detection with feature selection and scaling techniques using machine learning models.

Rakibul Hasan1, Barna Biswas2, Md Samiun3

  • 1Department of Business Administration, Westcliff University, 17877 Von Karman Ave 4th Floor, Irvine, CA, 92614, USA. r.hasan.179@westcliff.edu.

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|March 18, 2025
PubMed
Summary
This summary is machine-generated.

Robust malware detection is crucial. This study found that ensemble machine learning models, particularly Light Gradient Boosting Machine (LGBM), combined with Principal Component Analysis (PCA) and scaling, achieved over 97% accuracy in identifying malware.

Keywords:
Deep learningFeature scalingMachine learningMalware detectionPrincipal component analysis

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Data Science

Background:

  • Malware poses a significant and growing threat to digital security.
  • Effective cybersecurity relies on advanced and accurate malware detection systems.
  • Current detection methods require optimization through improved feature engineering and model selection.

Purpose of the Study:

  • To evaluate the influence of feature selection and scaling techniques on machine learning model performance for malware detection.
  • To compare the efficacy of various traditional and ensemble machine learning models in identifying malware.
  • To identify optimal preprocessing and modeling strategies for enhanced malware detection.

Main Methods:

  • Utilized a binary tabular classification dataset comprising 11,598 samples and 139 features.
  • Experimented with three feature scaling methods: no scaling, normalization, and min-max scaling.
  • Applied three feature selection techniques: no selection, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA).
  • Evaluated twelve machine learning models, including ensemble methods and traditional algorithms, using metrics like accuracy, precision, recall, F1-score, and AUC-ROC.

Main Results:

  • The Light Gradient Boosting Machine (LGBM) model achieved the highest accuracy (97.16%) when combined with Principal Component Analysis (PCA) and either min-max scaling or normalization.
  • Ensemble machine learning models consistently demonstrated superior performance compared to traditional machine learning models.
  • Feature selection and scaling significantly impacted the overall effectiveness of malware detection models.

Conclusions:

  • Principal Component Analysis (PCA) and appropriate feature scaling are vital for optimizing machine learning-based malware detection.
  • Ensemble models offer a more robust approach to malware detection than traditional algorithms.
  • The findings provide a roadmap for developing more reliable and efficient cybersecurity solutions against evolving malware threats.