Towards Design of a Novel Android Malware Detection Framework Using Hybrid Deep Learning Techniques
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-3590-9_15
Reference23 articles.
1. Zhou, Y., & Jiang, X. (2012). Dissecting android malware: Characterization and evolution. In 2012 IEEE Symposium on Security and Privacy (pp. 95–109). https://doi.org/10.1109/SP.2012.16
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3. Sabhadiya, S. (2019). Android malware detection using deep learning. In International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1254–1260)
4. Safa, H., Nassar, M., & Al Orabi, W. A. R. (2019, June). Benchmarking convolutional and recurrent neural networks for malware classification. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 561–566). IEEE.
5. Lin, Q. (2020). Classification of IoT malware based on convolutional neural network (pp. 51–57). https://doi.org/10.1109/ICSS50103.2020.00016
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