Tuning Deep Learning Performance for Android Malware Detection
Jarrett Booz, Josh McGiff, William Grant Hatcher, Wei Yu, James H. Nguyen, Chao Lu · 2018
In this paper, we address the issue of Android malware detection by implementing a deep learning environment and fine-tune parameters to determine optimal settings for the classification of Android malware from extracted permission data. By determining the optimal settings, we demonstrate the potential performance of a deep learning environment for Android malware detection. Specifically, we conduct an extensive study of various hyper-parameters to determine optimal configurations, and then carry out a performance evaluation on those configurations to compare and maximize detection accuracy in our target networks. Our results achieve approximately 95 % detection accuracy, with an approximate F1 score of 93 %.