Malware Classification Using Attention-Based Transductive Learning Network-Reference-Cited by-同舟云学术

Malware Classification Using Attention-Based Transductive Learning Network

Published:2020 Issue: Volume: Page:403-418
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Deng Liting,Wen Hui,Xin Mingfeng,Sun Yue,Sun Limin,Zhu Hongsong

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-63095-9_26

Reference26 articles.

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3. Raff, E., Barker, J., Sylvester, J., Brandon, R., Catanzaro, B., Nicholas, C.K.: Malware detection by eating a whole exe. In: Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence (2018)

4. Le, Q., Boydell, O., Mac Namee, B., Scanlon, M.: Deep learning at the shallow end: malware classification for non-domain experts. Digital Invest. 26, S118–S126 (2018)

5. Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1199–1208 (2018)

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