Malware Evasion Attack and Defense
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Apr 2019.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 2.
4 papers.
- 2022MERLIN -- Malware Evasion with Reinforcement LearnINgcited by 11
- 2024Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Camerascited by 3
- 2022Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditionscited by 1
- 2024On the Effectiveness of Adversarial Training on Malware Classifierscited by 1
- 2024On the Effectiveness of Adversarial Training on Malware Classifiers24 Dec 2024
- 2024Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras5 Nov 2024
- 2022Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions13 Apr 2022
- 2022MERLIN -- Malware Evasion with Reinforcement LearnINg24 Mar 2022