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arXiv 2007.002632020-07-01cs.CRcs.LG

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

Cyber Technology Institute（网络技术研究院）；Al-Qunfudah College of Computing（古尼富达计算学院）；Faculty of Computing, Engineering and Media（计算、工程与媒体学院）；Umm Al-Qura University（乌姆阿尔库拉大学）；De Montfort University（德蒙福特大学）

Suleiman Y. Yerima, Mohammed K. Alzaylaee

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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.

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