Deep Learning Techniques for Android Botnet Detection

Date

2021-02-23

Advisors

Journal Title

Journal ISSN

ISSN

2079-9292

Volume Title

Publisher

MDPI

Type

Article

Peer reviewed

Yes

Abstract

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.

Description

open access article

Keywords

deep learning, convolutional neural networks, gated recurrent unit, long short-term memory, recurrent neural networks, botnet detection, Android botnets, CNN-LSTM, CNN-GRU, Machine learning

Citation

Yerima, S.Y., Alzaylaee, M.K., Shajan, A., P, V. (2021) Deep Learning Techniques for Android Botnet Detection. Electronics, 10(4), 519.

Rights

Research Institute

Cyber Technology Institute (CTI)