Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks

Matouš Kozák () and Martin Jureček ()
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Matouš Kozák: Czech Technical University in Prague, Faculty of Information Technology
Martin Jureček: Czech Technical University in Prague, Faculty of Information Technology

A chapter in Machine Learning, Deep Learning and AI for Cybersecurity, 2025, pp 267-290 from Springer

Abstract: Abstract Adversarial attacks present significant challenges for malware detection systems. This research investigates the effectiveness of benign and malicious adversarial examples (AEs) in evasion and poisoning attacks on the Portable Executable file domain. A novel focus of this study is on benign AEs, which, although not directly harmful, can increase false positives and undermine trust in antivirus solutions. We propose modifying existing adversarial malware generators to produce benign AEs and show they are as successful as malware AEs in evasion attacks. Furthermore, our data show that benign AEs have a more decisive influence in poisoning attacks than standard malware AEs, demonstrating their superior ability to decrease the model’s performance. Our findings introduce new opportunities for adversaries and further increase the attack surface that needs to be protected by security researchers.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-83157-7_10

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DOI: 10.1007/978-3-031-83157-7_10

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