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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F25%3A00385368" target="_blank" >RIV/68407700:21240/25:00385368 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-83157-7_10" target="_blank" >https://doi.org/10.1007/978-3-031-83157-7_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-83157-7_10" target="_blank" >10.1007/978-3-031-83157-7_10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Machine Learning, Deep Learning and AI for Cybersecurity

ISBN

978-3-031-83156-0

Počet stran výsledku

24

Strana od-do

267-290

Počet stran knihy

642

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Basel

Kód UT WoS kapitoly

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