Deep Neural Networks for Android Malware Detection
Abhilash Hota, Paul Irolla · 2019
In this paper we present a study of the application of deep neural networks to the problem of pattern matching in Android malware detection. Over the last few years malware have been proliferating and malware authors keep developing new techniques to bypass existing detection methods. Machine learning techniques in general and deep neural networks in particular have been very successful in recent years in a variety of classification tasks. We study various deep neural networks as potential solutions for pattern matching in malware detection systems. The effectiveness of the different architectures is compared and judged as a potential replacement for traditional approaches to malware detection in Android systems.