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

In: Machine Learning, Deep Learning and AI for Cybersecurity

Author

Listed:
  • Matouš Kozák

    (Czech Technical University in Prague, Faculty of Information Technology)

  • Martin Jureček

    (Czech Technical University in Prague, Faculty of Information Technology)

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.

Suggested Citation

  • Matouš Kozák & Martin Jureček, 2025. "Effectiveness of Adversarial Benign and Malware Examples in Evasion and Poisoning Attacks," Springer Books, in: Mark Stamp & Martin Jureček (ed.), Machine Learning, Deep Learning and AI for Cybersecurity, pages 267-290, Springer.
  • Handle: RePEc:spr:sprchp:978-3-031-83157-7_10
    DOI: 10.1007/978-3-031-83157-7_10
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