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Verwer, S.E. (author), Nadeem, A. (author), Hammerschmidt, C.A. (author), Bliek, L. (author), Al-Dujaili, Abdullah (author), O’Reilly, Una-May (author)
Training classifiers that are robust against adversarially modified examples is becoming increasingly important in practice. In the field of malware detection, adversaries modify malicious binary files to seem benign while preserving their malicious behavior. We report on the results of a recently held robust malware detection challenge. There...
conference paper 2020