{"ID":2847004,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00894","arxiv_id":"2511.00894","title":"Android Malware Detection: A Machine Leaning Approach","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.","short_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....","url_abs":"https://arxiv.org/abs/2511.00894","url_pdf":"https://arxiv.org/pdf/2511.00894v1","authors":"[\"Hasan Abdulla\"]","published":"2025-11-02T11:26:31Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}