MiBeX: Malware-Inserted Benign Datasets for Explainable Machine Learning

Author:

Stegner Wayne,Westland Tyler,Kapp David,Kebede Temesguen,Jha Rashmi

Publisher

Springer International Publishing

Reference36 articles.

1. Kaspersky. Kaspersky Lab Number of the Year: 360,000 Malicious Files Detected Daily in 2017 2017. https://usa.kaspersky.com/about/press-releases/ 2017

2. Kancherla, K., Mukkamala, S.: Image visualization based malware detection. In: 2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS). IEEE, pp. 40–44 (2013). ISBN: 978-1-4673-5867-5. https://ieeexplore.ieee.org/document/6597204

3. Santacroce, M.L., Koranek, D., ha, R.: Detecting Malware code as video with compressed, time-distributed neural networks. IEEE Access 8, 132748–132760 (2020). ISSN: 2169-3536. https://ieeexplore.ieee.org/document/9145735/

4. Santacroce, M., Stegner, W., Koranek, D., Jha, R.: A foray into etracting malicious features from executable code with neural network salience. In: Proceedings of the IEEE National Aerospace Electronics Conference, NAECON 2019-July, pp. 185–191. IEEE. ISBN: 9781728114163. https://ieeexplore.ieee.org/document/9057859/

5. Raff, E., et al.: Malware Detection by Eating a Whole EXE. arXiv:1710.09435. http://arxiv.org/abs/1710.09435 (2017)

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