Malware Classification Based on Semi-Supervised Learning

Author:

Ding Yu,Zhang XiaoYu,Li BinBin,Xing Jian,Qiang Qian,Qi ZiSen,Guo MengHan,Jia SiYu,Wang HaiPing

Publisher

Springer International Publishing

Reference25 articles.

1. AMR: Kaspersky security bulletin 2021. statistics. https://securelist.com/kaspersky-security-bulletin-2021-statistics/105205/. Accessed 15 Dec 2021

2. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)

3. Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E.: Big self-supervised models are strong semi-supervised learners. In: Advances in Neural Information Processing Systems, vol. 33, pp. 22243–22255 (2020)

4. Ding, C., Luktarhan, N., Lu, B., Zhang, W.: A hybrid analysis-based approach to android malware family classification. Entropy 23(8), 1009 (2021)

5. Duarte-Garcia, H.L., et al.: A semi-supervised learning methodology for malware categorization using weighted word embeddings. In: 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS &PW), pp. 238–246. IEEE (2019)

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