Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based method for detecting Android botnets by analyzing static app features, demonstrating superior accuracy over existing machine learning approaches on a large dataset.

Contribution

The paper presents a novel deep learning model using CNNs trained on static app features for effective Android botnet detection, outperforming prior methods.

Findings

CNN achieved highest prediction accuracy among tested classifiers.

Model outperformed previous machine learning approaches.

Effective detection on large real-world dataset.

Abstract

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.