  Today, Android devices are able to provide various services. They support
applications for different purposes such as entertainment, business, health,
education, and banking services. Because of the functionality and popularity of
Android devices as well as the open-source policy of Android OS, they have
become a suitable target for attackers. Android Botnet is one of the most
dangerous malwares because an attacker called Botmaster can control that
remotely to perform destructive attacks. A number of researchers have used
different well-known Machine Learning (ML) methods to recognize Android Botnets
from benign applications. However, these conventional methods are not able to
detect new sophisticated Android Botnets. In this paper, we propose a novel
method based on Android permissions and Convolutional Neural Networks (CNNs) to
classify Botnets and benign Android applications. Being the first developed
method that uses CNNs for this aim, we also proposed a novel method to
represent each application as an image which is constructed based on the
co-occurrence of used permissions in that application. The proposed CNN is a
binary classifier that is trained using these images. Evaluating the proposed
method on 5450 Android applications consist of Botnet and benign samples, the
obtained results show the accuracy of 97.2% and recall of 96% which is a
promising result just using Android permissions.
