DroidAutoML: An AutoML Tool for the Android Malware Detection Domain

• Joner Assolin UFAM http://orcid.org/0000-0001-8517-6600
• Diego Kreutz UNIPAMPA https://orcid.org/0000-0003-0830-0238
• Guilherme Siqueira UNIPAMPA https://orcid.org/0000-0002-8621-0660
• Vanderson Rocha UFAM https://orcid.org/0000-0003-3103-7749
• Charles Miers UDESC https://orcid.org/0000-0002-1976-0478
• Rodrigo Mansilha UNIPAMPA https://orcid.org/0000-0002-2083-653X
• Eduardo Feitosa UFAM https://orcid.org/0000-0001-6401-3992

DOI: https://doi.org/10.5753/sbseg_estendido.2022.227037

Abstract

In this work we present DroidAutoML, an AutoML tool for generating models for Android malware detection. From an input dataset and a data pipeline of four stages, our tool automatically generates optimized predictive models. Our initial findings show DroidAutoML generates very good models, reaching recall levels of 95%, for instance.

Keywords: AutoML, Android Malware Detection, Dataset, Tool

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