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1 2025

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Comparative features reduction investigation for Android malware detection on Boolean data

Irawan Ary, Bilski Piotr, Gwardys Grzegorz

International Journal of Electronics and Telecommunications

2025

Vol. 71, No. 4

The aim of this research is to enhance the effectiveness of Android malware detection systems by implementing dimensionality reduction techniques on Boolean data. Algorithms such as Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and Multi-Correspondence Analysis (MCA) serve as operations preceding the classification stage. The analysis is carried out using multiple classifiers such as Random Forest Classifier, Logistic Regression, and Support Vector Machines to measure how effective they can detect cyber threats. Results show that the Decision Tree Classifier, implemented without dimensionality reduction, achieved the optimal results with 100% accuracy. Efficient feature selection and rapid computation in the context of malware detection are necessary for real-time mobile cyber environment applications.