Malware Evasion Attack and Defense
This work addresses the problem of adversarial vulnerabilities in malware detection systems for cybersecurity applications, but it appears incremental as it builds on existing evasion attack and defense methods.
The paper investigates white-box and grey-box evasion attacks on an ML-based malware detector, evaluating performance in a real-world setting and comparing defense approaches to mitigate these attacks.
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.