  Android, being the most widespread mobile operating systems is increasingly
becoming a target for malware. Malicious apps designed to turn mobile devices
into bots that may form part of a larger botnet have become quite common, thus
posing a serious threat. This calls for more effective methods to detect
botnets on the Android platform. Hence, in this paper, we present a deep
learning approach for Android botnet detection based on Convolutional Neural
Networks (CNN). Our proposed botnet detection system is implemented as a
CNN-based model that is trained on 342 static app features to distinguish
between botnet apps and normal apps. The trained botnet detection model was
evaluated on a set of 6,802 real applications containing 1,929 botnets from the
publicly available ISCX botnet dataset. The results show that our CNN-based
approach had the highest overall prediction accuracy compared to other popular
machine learning classifiers. Furthermore, the performance results observed
from our model were better than those reported in previous studies on machine
learning based Android botnet detection.
