Team 4 Research: Deep Learning Algorithms for Android Malware Detection

November 17, 2020 vemamian Leave a comment

Deep Learning Algorithms for Android Malware Detection

Ouda Adomuha 1, Alalmai M. Abdulhadi 1, Alqahtani S. Ibrahim1, Ferenczi Tamas 1, Garza I. Jose 1

Vahid Emamian 2, Senior IEEE Member

1Computer Science Department

2Electrical Engineering Department

St. Mary’s University, San Antonio TX

Abstract: Android, an open source software designed for mobile devices such as smartphones and tablets with primarily touchscreen as input interface has grown exponentially in the last decade. This growth has been a catalyst for the increased rate of malware attacks on these types of systems. Since traditional antivirus software have been deficient at tackling the dynamic nature of attacks on them, deep learning was introduced and discovered to be a better approach in dealing with this challenge. Deep learning utilizes static, dynamic and hybrid approaches in the analysis of malware. Several literatures were reviewed to examine the efficacy of the system and discovered to still be lacking in some respect though much more promising than traditional antivirus software.