Deep Learning Techniques for Android Botnet Detection

Electronics

MohammedK. Alzaylaee¹  SuleimanY. Yerima²  Vinod P³  Annette Shajan⁴ 

[1] College of Computing in Al-Qunfudhah, Umm Al-Qura University, Mecca 21955, Saudi Arabia;Cyber Technology Institute, De Montfort University, Leicester LE1 9BH, UK;Department of Computer Applications, Cochin University of Science and Technology, Cochin 682022, India;RV College of Engineering, Bengaluru 560059, India;

关键词: botnet detection;    deep learning;    Android botnets;    convolutional neural networks;    dense neural networks;    recurrent neural networks;   

DOI  :  10.3390/electronics10040519

来源: DOAJ

【 摘 要 】

Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive malware families, such as Chamois, designed to turn Android devices into bots that form part of a larger botnet are becoming prevalent. This calls for more effective methods for detection of Android botnets. Recently, deep learning has gained attention as a machine learning based approach to enhance Android botnet detection. However, studies that extensively investigate the efficacy of various deep learning models for Android botnet detection are currently lacking. Hence, in this paper we present a comparative study of deep learning techniques for Android botnet detection using 6802 Android applications consisting of 1929 botnet applications from the ISCX botnet dataset. We evaluate the performance of several deep learning techniques including: CNN, DNN, LSTM, GRU, CNN-LSTM, and CNN-GRU models using 342 static features derived from the applications. In our experiments, the deep learning models achieved state-of-the-art results based on the ISCX botnet dataset and also outperformed the classical machine learning classifiers.

【 授权许可】

Unknown