loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Matouš Kozák and Martin Jureček

Affiliation: Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, Prague, Czech Republic

Keyword(s): Adversarial Examples, Malware Detection, Static Analysis, PE Files, Machine Learning.

Abstract: 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 genera tors, 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.239.9.151

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kozák, M. and Jureček, M. (2023). Combining Generators of Adversarial Malware Examples to Increase Evasion Rate. In Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-666-8; ISSN 2184-7711, SciTePress, pages 778-786. DOI: 10.5220/0012127700003555

@conference{secrypt23,
author={Matouš Kozák. and Martin Jureček.},
title={Combining Generators of Adversarial Malware Examples to Increase Evasion Rate},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT},
year={2023},
pages={778-786},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012127700003555},
isbn={978-989-758-666-8},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT
TI - Combining Generators of Adversarial Malware Examples to Increase Evasion Rate
SN - 978-989-758-666-8
IS - 2184-7711
AU - Kozák, M.
AU - Jureček, M.
PY - 2023
SP - 778
EP - 786
DO - 10.5220/0012127700003555
PB - SciTePress