Combining Generators of Adversarial Malware Examples to Increase Evasion Rate

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Combining Generators of Adversarial Malware Examples to Increase Evasion Rate

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

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

Publication year

2023

Confidentiality

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

Article name in the collection

Proceedings of the 20th International Conference on Security and Cryptography

ISBN

978-989-758-666-8

ISSN

2184-7711

e-ISSN

—

Number of pages

9

Pages from-to

778-786

Publisher name

SciTePress - Science and Technology Publications

Place of publication

Porto

Event location

Řím

Event date

Jul 10, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001072829100080