Paper Title
Mobile Botnet Sentinel Using CNN (Deep Learning Approach)

Abstract
Abstract - Android, being the most widespread mobile applications, is increasingly becoming the target of malware. Malicious applications that are designed to turn mobile devices into bots that can become part of a larger botnet are becoming more common, thus posing a serious threat. This requires the most efficient ways to get botnet on the Android platform. Therefore, in this project, we are using an in-depth learning method for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is used as a CNN-based model trained in 342 static application features to distinguish between botnet applications and standard applications. Keywords - Botnet detection;Android Botnets; Deep learning; Convolutional; Neural Networks; Machine learning; Android Botnets