  In the past decade, the cyber-crime related to mobile devices has increased.
Mobile devices, especially the ones running on Android operating system are
particularly interesting to malware creators, as the users often keep the
biggest amount of personal information on their mobile devices, such as their
contacts, social media profiles, emails, and bank accounts. Both dynamic and
static malware analysis is necessary to prevent and detect malware, as both
techniques have their benefits and shortcomings. In this paper, we propose a
deep learning technique that relies on LSTM and encoder-decoder neural network
architectures for dynamic malware analysis based on CPU, memory and battery
usage. The proposed system is able to detect and notify users about anomalies
in system that is likely consequence of malware behaviour. The method was
implemented as a part of OWASP Seraphimdroids anti-malware mechanism and
notifies users about anomalies on their devices. The method proved to perform
with an F1-score of 79.2%.
