Malware Evasion Attack and Defense

Authors: Yonghong Huang, Utkarsh Verma, Celeste Fralick, Gabriel Infante-Lopez, Brajesh Kumarz, Carl Woodward
Published: 2019-04-07 | Updated: 2019-04-16

Source: https://arxiv.org/abs/1904.05747

PDF: https://arxiv.org/pdf/1904.05747

AIにより推定されたラベル

マルウェア検出 敵対的サンプル 敵対的攻撃検出

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract

Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasion attacks to an ML-based malware detector and conduct performance evaluations in a real-world setting. We compare the defense approaches in mitigating the attacks. We propose a framework for deploying grey-box and black-box attacks to malware detection systems.