Fast and Efficient Malware Detection with Joint Static and Dynamic Features Through Transfer Learning-Reference-Cited by-同舟云学术

Fast and Efficient Malware Detection with Joint Static and Dynamic Features Through Transfer Learning

Published:2023 Issue: Volume: Page:503-531
ISSN:0302-9743
Container-title:Applied Cryptography and Network Security
language:
Short-container-title:

Author:

Ngo Mao V.,Truong-Huu Tram,Rabadi Dima,Loo Jia Yi,Teo Sin G.

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-33488-7_19

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3. Bucila, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Proceedings of ACM SIGKDD, pp. 535–541. KDD 2006, ACM, New York, NY, USA (2006)

4. contributors, T.X.: Extreme gradient boosting open-source software library. https://xgboost.readthedocs.io/en/latest/parameter.html. Accessed 12 Mar 2022

5. Damodaran, A., Di Troia, F., Visaggio, C.A., Austin, T.H., Stamp, M.: A comparison of static, dynamic, and hybrid analysis for malware detection. J. Comput. Virol. Hack. Techn. 13(1), 1–12 (2017)

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