The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00371216" target="_blank" >RIV/68407700:21230/24:00371216 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-51482-1_3" target="_blank" >https://doi.org/10.1007/978-3-031-51482-1_3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-51482-1_3" target="_blank" >10.1007/978-3-031-51482-1_3</a>

Result language

angličtina

Original language name

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning

Original language description

Due to the proliferation of malware, defenders are increasingly turning to automation and machine learning as part of the malware detection toolchain. However, machine learning models are susceptible to adversarial attacks, requiring the testing of model and product robustness. Meanwhile, attackers also seek to automate malware generation and evasion of antivirus systems, and defenders try to gain insight into their methods. This work proposes a new algorithm that combines Malware Evasion and Model Extraction (MEME) attacks. MEME uses model-based reinforcement learning to adversarially modify Windows executable binary samples while simultaneously training a surrogate model with a high agreement with the target model to evade. To evaluate this method, we compare it with two state-of-the-art attacks in adversarial malware creation, using three well-known published models and one antivirus product as targets. Results show that MEME outperforms the state-of-the-art methods in terms of evasion capabilities in almost all cases, producing evasive malware with an evasion rate in the range of 32–73%. It also produces surrogate models with a prediction label agreement with the respective target models between 97–99%. The surrogate could be used to fine-tune and improve the evasion rate in the future.

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/VJ02010020" target="_blank" >VJ02010020: AI-Dojo: Multiagent Testbed for Research and Testing of AI-driven Cybersecurity Technologies</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

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

Article name in the collection

28th European Symposium on Research in Computer Security, The Hague, The Netherlands, September 25–29, 2023, Proceedings, Part I

ISBN

978-3-031-50593-5

ISSN

0302-9743

e-ISSN

—

Number of pages

21

Pages from-to

44-64

Publisher name

Springer Nature Switzerland AG

Place of publication

Basel

Event location

The Hague

Event date

Sep 25, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001208360100003