Deep Learning Techniques for Android Botnet Detection

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 meth...

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Bibliographic Details
Published in:Electronics (Basel) 2021-02, Vol.10 (4), p.519
Main Authors: Yerima, Suleiman Y., Alzaylaee, Mohammed K., Shajan, Annette, P, Vinod
Format: Article
Language:English
Subjects:
Accuracy
Comparative studies
Datasets
Decision trees
Deep learning
Machine learning
Malware
Mobile commerce
Mobile operating systems
Neural networks
Performance evaluation
Temporal logic
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10040519