<ul><li>A novel framework is introduced to enhance Android malware detection and classification using an attention-enhanced Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM).</li><li>The framework achieves an impressive accuracy of over 99% by analyzing only 47 features out of over 9,760 available in the dataset.</li><li>The MLP component, enhanced with an attention mechanism, focuses on discriminative features and reduces the feature set to 14 components using Linear Discriminant Analysis (LDA).</li><li>The SVM component, utilizing an RBF kernel, accurately maps the reduced components to a high-dimensional space for precise classification of malware into their respective families.</li></ul>

Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach

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