Malware Evasion Attack and Defense

2019/04/07 by Yonghong Huang, Utkarsh Verma, Huang, Yonghong +9
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Network Security and Intrusion Detection

paper · pdf · doi:10.48550/arxiv.1904.05747

openalex publication_date 2019/04/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• A Survey on Adversarial Attacks for Malware Analysis
• MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers
• Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification
• Exploring Adversarial Examples in Malware Detection
• Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge