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Deep Learning Techniques for Android Botnet Detection
| Content Provider | MDPI |
|---|---|
| Author | Vinod, P. Yerima, Suleiman Alzaylaee, Mohammed Shajan, Annette |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 519 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics10040519 |
| Journal | Electronics |
| Issue Number | 4 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-02-23 |
| Access Restriction | Open |
| Subject Keyword | Electronics Computation Theory and Mathematics Botnet Detection Deep Learning Android Botnets Convolutional Neural Networks Dense Neural Networks Recurrent Neural Networks Long Short-term Memory Gated Recurrent Unit Cnn-lstm Cnn-gru |
| Content Type | Text |
| Resource Type | Article |
