Comparative Features Reduction Investigation for Android Malware Detection on Boolean Data
Abstract
The objective of this research is to enhance the effectiveness of Android malware detection systems by implementing dimensionality reduction techniques on binary 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 Machine to measure how effective they can detect cyber threats. From the results obtained, it is known that the Decision Tree Classifier, implemented without dimensionality reduction, achieved the most optimal results, exhibiting 100% accuracy. The results highlight the need for efficient feature selection and rapid computation in the context of malware detection system design, which are necessary for real-time mobile cyber environment applications.
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