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Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network

  • Wei WangEmail author
  • Mengxue Zhao
  • Jigang Wang
Original Research

Abstract

Android security incidents occurred frequently in recent years. To improve the accuracy and efficiency of large-scale Android malware detection, in this work, we propose a hybrid model based on deep autoencoder (DAE) and convolutional neural network (CNN). First, to improve the accuracy of malware detection, we reconstruct the high-dimensional features of Android applications (apps) and employ multiple CNN to detect Android malware. In the serial convolutional neural network architecture (CNN-S), we use Relu, a non-linear function, as the activation function to increase sparseness and “dropout” to prevent over-fitting. The convolutional layer and pooling layer are combined with the full-connection layer to enhance feature extraction capability. Under these conditions, CNN-S shows powerful ability in feature extraction and malware detection. Second, to reduce the training time, we use deep autoencoder as a pre-training method of CNN. With the combination, deep autoencoder and CNN model (DAE-CNN) can learn more flexible patterns in a short time. We conduct experiments on 10,000 benign apps and 13,000 malicious apps. CNN-S demonstrates a significant improvement compared with traditional machine learning methods in Android malware detection. In details, compared with SVM, the accuracy with the CNN-S model is improved by 5%, while the training time using DAE-CNN model is reduced by 83% compared with CNN-S model.

Keywords

Deep learning Convolutional neural network Autoencoder Malware detection Android applications 

Notes

Acknowledgements

The work reported in this paper was supported in part by National Key R&D Program of China, under Grant 2017YFB0802805, in part by Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, under Grant AGK2015002, in part by ZTE Corporation Foundation, under Grant K17L00190, in part by funds of Science and Technology on Electronic Information Control Laboratory, under Grant K16GY00040, in part by the Fundamental Research funds for the central Universities of China, under Grant K17JB00060 and K17JB00020, and in part by Natural Science Foundation of China, under Grant U1736114 and 61672092.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Security and Privacy in Intelligent TransportationBeijing Jiaotong UniversityBeijingChina
  2. 2.Science and Technology on Electronic Information Control LaboratoryChengduChina
  3. 3.ZTE CorporationChengduChina

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