Android Malware Detection: A Machine Leaning Approach

• URL: http://arxiv.org/abs/2511.00894v1
• Date: Sun, 02 Nov 2025 11:26:31 GMT
• Title: Android Malware Detection: A Machine Leaning Approach
• Authors: Hasan Abdulla,
• Abstract summary: This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware.<n>The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and real-world applicability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and real-world applicability. Key findings show that ensemble methods demonstrate superior performance, but there are trade-offs between model interpretability, efficiency, and accuracy. Given its increasing threat, the insights guide future research and practical use of ML to combat Android malware.

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