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Malware classification is a difficult problem, to which machine learning methods have been applied for decades.
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2018
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2019
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2019
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R. S. Geiger, K. Yu, Y. Yang, M. Dai, J. Qiu, R. Tang, and J. Huang, “Garbage in, Garbage out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?” in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , ser. FAT* ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 325–336. [Online]. Available: https://doi.org/10.1145/3351095.3372862
2020
Closest in time.
S. Zhu, J. Shi, L. Yang, B. Qin, Z. Zhang, L. Song, and G. Wang, “Measuring and Modeling the Label Dynamics of Online Anti-Malware Engines,” in 29th USENIX Security Symposium (USENIX Security 20) . Boston, MA: {USENIX} Association, 8 2020. [Online]. Available: https://www.usenix.org/conference/usenixsecurity20/presentation/zhu
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2020
Closest in time.
H. Aghakhani, F. Gritti, F. Mecca, M. Lindorfer, S. Ortolani, D. Balzarotti, G. Vigna, and C. Kruegel, “When Malware is Packin’ Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features,” in Proceedings 2020 Network and Distributed System Security Symposium . Reston, VA: Internet Society, 2020. [Online]. Available: https://www.ndss-symposium.org/wp-content/uploads/2020/02/24310.pdf
2020
Closest in time.