An Android Malware Detection Method Based on Native Libraries-Reference-Cited by-同舟云学术

An Android Malware Detection Method Based on Native Libraries

Published:2020-08-02 Issue: Volume: Page:233-246
ISSN:2194-5357
Container-title:Smart Innovations in Communication and Computational Sciences
language:
Short-container-title:

Author:

Zhu Qian,Xu Yuanqi,Jiang Chenyang,Xie Wenling

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-5345-5_21

Reference10 articles.

1. Karbab, E.B., Debbabi, M., Derhab, A., Mouheb, D.: MalDozer: automatic framework for android malware detection using deep learning. Digital Investigation, 24, S48–S59

2. Fan, M., Liu, J., Luo, X., Chen, K., Tian, Z., Zheng, Q., Liu, T.: Android malware familial classific-tion and representative sample selection via frequent subgraph analysis. IEEE Trans. Inf. Forensics Secur. 13(8), 1890–1905 (2018)

3. Chen, P., Zhao, R., Shan, Z., Han, J., Meng, X.: Android malware behavior similarity detection based on dynamic and static combination. Appl. Res. Comput. (5), 1534–1539

4. Faruki, P., Laxmi, V., Bharmal, A., Gaur, M.S., Ganmoor, V.: ANDROSIMILAR: robust signa-ture for detecting variants of Android malware. J. Inf. Secur. Appl. 22, 66–80 (2015)

5. Lee, W.Y., Saxe, J., Harang, R.: SeqDroid: obfuscated android malware detection using stacked convolutional and recurrent neural networks. In: Deep Learning Applications for Cyber Security, pp. 197–210. Springer, Cham

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Systematic Review and Future Perspective of Android Malware Detection Based Machine Learning Techniques;2023 IEEE International Conference on ICT in Business Industry & Government (ICTBIG);2023-12-08

2. Android malware classification using convolutional neural network and LSTM;Journal of Computer Virology and Hacking Techniques;2021-04-29