Creating valid adversarial examples of malware

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00374597" target="_blank" >RIV/68407700:21240/24:00374597 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s11416-024-00516-2" target="_blank" >https://doi.org/10.1007/s11416-024-00516-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11416-024-00516-2" target="_blank" >10.1007/s11416-024-00516-2</a>

Result language

angličtina

Original language name

Creating valid adversarial examples of malware

Original language description

Because of its world-class results, machine learning (ML) is becoming increasingly popular as a go-to solution for many tasks. As a result, antivirus developers are incorporating ML models into their toolchains. While these models improve malware detection capabilities, they also carry the disadvantage of being susceptible to adversarial attacks. Although this vulnerability has been demonstrated for many models in white-box settings, a black-box scenario is more applicable in practice for the domain of malware detection. We present a method of creating adversarial malware examples using reinforcement learning algorithms. The reinforcement learning agents utilize a set of functionality-preserving modifications, thus creating valid adversarial examples. Using the proximal policy optimization (PPO) algorithm, we achieved an evasion rate of 53.84% against the gradient-boosted decision tree (GBDT) detector. The PPO agent previously trained against the GBDT classifier scored an evasion rate of 11.41% against the neural network-based classifier MalConv and an average evasion rate of 2.31% against top antivirus programs. Furthermore, we discovered that random application of our functionality-preserving portable executable modifications successfully evades leading antivirus engines, with an average evasion rate of 11.65%. These findings indicate that ML-based models used in malware detection systems are sensitive to adversarial attacks and that better safeguards need to be taken to protect these systems.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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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

2024

Confidentiality

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

Name of the periodical

Journal of Computer Virology and Hacking Techniques

ISSN

2263-8733

e-ISSN

—

Volume of the periodical

20

Issue of the periodical within the volume

4

Country of publishing house

FR - FRANCE

Number of pages

15

Pages from-to

607-621

UT code for WoS article

001186154300001

EID of the result in the Scopus database

2-s2.0-85187924754