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Adversarial Patterns: Building Robust Android Malware Classifiers. (arXiv:2203.02121v1 [cs.CR])
March 7, 2022, 2:20 a.m. | Dipkamal Bhusal, Nidhi Rastogi
cs.CR updates on arXiv.org arxiv.org
Deep learning-based classifiers have substantially improved recognition of
malware samples. However, these classifiers can be vulnerable to adversarial
input perturbations. Any vulnerability in malware classifiers poses significant
threats to the platforms they defend. Therefore, to create stronger defense
models against malware, we must understand the patterns in input perturbations
caused by an adversary. This survey paper presents a comprehensive study on
adversarial machine learning for android malware classifiers. We first present
an extensive background in building a machine learning classifier …
More from arxiv.org / cs.CR updates on arXiv.org
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