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Machine learning is becoming the latest trend by making growing use of it in various fields of application. Along with that, the invasions designed to evade these systems has also evolved apparently. The adversarial attacks are becoming the major threats for developing threats to these models. Current training pipelines for the classification of malware based on machine learning (ML) depend on crowdsourced threat feeds which expose a normal point of injection. We research the vulnerability of ML malware classifications to backdoor poisoning attack for the first time, concentrating explicitly on stimulating “clean label” attacks where attackers donate monitor the mechanism of sample classification. In this paper, we reviewed various attacks based on machine learning models and their working strategies. We also discussed the threat models and backdoor attacks on malware classifiers.

Exploring adversarial attacks against malware classifiers in the backdoor poisoning attack