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Published in:
2023 | OriginalPaper | Chapter
11. Towards Robustness
Author : Raphael Labaca-Castro
Published in: Machine Learning under Malware Attack
Publisher: Springer Fachmedien Wiesbaden
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Abstract
Having shown that ML models are susceptible to adversarial examples, we now explore potential strategies to harden malware classifiers, as displayed under Defenses in Fig. 1.1. In general, the success metric evaluated is based on minimizing the FNR, that is, the number of adversarial examples that effectively bypass the classifier. By measuring the baseline evasion rate and comparing it to the hardened model, we can establish the success of the defense mechanism implemented.