Malware Evasion Attack and Defense

被引：11
|
作者：
Huang, Yonghong [1 ]
Verma, Utkarsh [1 ]
Fralick, Celeste [1 ]
Infante-Lopez, Gabriel [2 ]
Kumar, Brajesh [3 ]
Woodward, Carl [1 ]
机构：
[1] McAfee, Plano, TX 75024 USA
[2] McAfee, Cordoba, Argentina
[3] McAfee, Hyderabad, Telangana, India
来源：
2019 49TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS (DSN-W) | 2019年
关键词：
Adversarial machine learning; adversarial examples; evasion attack; defense;
D O I：
10.1109/DSN-W.2019.00014
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：34 / 38
页数：5
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