Combining Generators of Adversarial Malware Examples to Increase Evasion Rate

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00367091" target="_blank" >RIV/68407700:21240/23:00367091 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scitepress.org/Link.aspx?doi=10.5220/0012127700003555" target="_blank" >https://www.scitepress.org/Link.aspx?doi=10.5220/0012127700003555</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0012127700003555" target="_blank" >10.5220/0012127700003555</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining Generators of Adversarial Malware Examples to Increase Evasion Rate

Popis výsledku v původním jazyce

Antivirus developers are increasingly embracing machine learning as a key component of malware defense. While machine learning achieves cutting-edge outcomes in many fields, it also has weaknesses that are exploited by several adversarial attack techniques. Many authors have presented both white-box and black-box generators of adversarial malware examples capable of bypassing malware detectors with varying success. We propose to combine contemporary generators in order to increase their potential. Combining different generators can create more sophisticated adversarial examples that are more likely to evade anti-malware tools. We demonstrated this technique on five well-known generators and recorded promising results. The best-performing combination of AMG-random and MAB-Malware generators achieved an average evasion rate of 15.9% against top-tier antivirus products. This represents an average improvement of more than 36% and 627% over using only the AMG-random and MAB-Malware generators, respectively. The generator that benefited the most from having another generator follow its procedure was the FGSM injection attack, which improved the evasion rate on average between 91.97% and 1,304.73%, depending on the second generator used. These results demonstrate that combining different generators can significantly improve their effectiveness against leading antivirus programs.

Název v anglickém jazyce

Combining Generators of Adversarial Malware Examples to Increase Evasion Rate

Popis výsledku anglicky

Antivirus developers are increasingly embracing machine learning as a key component of malware defense. While machine learning achieves cutting-edge outcomes in many fields, it also has weaknesses that are exploited by several adversarial attack techniques. Many authors have presented both white-box and black-box generators of adversarial malware examples capable of bypassing malware detectors with varying success. We propose to combine contemporary generators in order to increase their potential. Combining different generators can create more sophisticated adversarial examples that are more likely to evade anti-malware tools. We demonstrated this technique on five well-known generators and recorded promising results. The best-performing combination of AMG-random and MAB-Malware generators achieved an average evasion rate of 15.9% against top-tier antivirus products. This represents an average improvement of more than 36% and 627% over using only the AMG-random and MAB-Malware generators, respectively. The generator that benefited the most from having another generator follow its procedure was the FGSM injection attack, which improved the evasion rate on average between 91.97% and 1,304.73%, depending on the second generator used. These results demonstrate that combining different generators can significantly improve their effectiveness against leading antivirus programs.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 20th International Conference on Security and Cryptography

ISBN

978-989-758-666-8

ISSN

2184-7711

e-ISSN

—

Počet stran výsledku

9

Strana od-do

778-786

Název nakladatele

SciTePress - Science and Technology Publications

Místo vydání

Porto

Místo konání akce

Řím

Datum konání akce

10. 7. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001072829100080